EASSS 2012, profile picture
Uploaded byEASSS 2012
812 views

T2. Organization and Environment oriented programming

The document provides an outline for a course on Organization and Environment Oriented Programming (EOP). It introduces EOP and compares it to Object Oriented Programming (OOP) and Environment Oriented Programming (EOP). It discusses key concepts in multi-agent systems including individual agent levels, environment levels, and social/organizational levels. It also summarizes the BDI agent architecture and various programming languages used for cognitive agents and multi-agent systems.

Embed presentation

Downloaded 21 times
Organisation and Environment Oriented Programming Outline of the course Introduction Olivier Boissier1 , Rafael H. Bordini2 , EOP Jomi F. H¨bner3 , Alessandro Ricci4 u About Environment Oriented Programming 1 EMSE, 2 PUC-RS, A&A and CArtAgO France Brazil Olivier.Boissier@emse.fr R.Bordini@pucrs.br OOP 3 DAS-UFSC, Brazil 4 University of Bologna, Italy About Organisation Oriented Programming jomi@das.ufsc.br a.ricci@unibo.it Moise Conclusions European Agent Systems Summer School Practical Exercise: a hands-on lab session! May, 2012 — Valencia, Spain Introduction EOP OOP Conclusion Abstractions in Multi-Agent Systems schema mission role ORGAMISATION org LEVEL Introduction agent AGENT LEVEL artifact ENDOGENOUS ENVIRONMENT wsp LEVEL EXOGENOUS ENVIRONMENT 4 / 165 network node Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion Abstractions in Multi-Agent Systems Agent Oriented Programming Individual Agent Level: autonomy, situatedness Cognitive Concepts: beliefs, desires, goals, intentions, plans Proposed by Shoham [Shoham, 1993] Reasoning Cycle: sense/reason/act, reactive/pro-active Use of mentalistic notions and a societal view of behaviour computation (anthropomorphism) Environment Level: resources and services that agents can Levels of abstraction: Agents – Organisations – Environment access and control; sensing and acting in an environment Programming languages for agents have developed a lot Social and Organisation Level: cooperation, coordination, since then, but still not a mature paradigm regulation patterns Programming languages/platforms for organisation and Roles: rights, responsibilities, ... environment are also being developed Organisational Rules: constraints on roles and their interactions, norms, deadlines, ... Some agent development platforms have a formal basis Organisational Structures: topology of interaction patterns Many influenced by the BDI agent architecture and relations over activity control 5 / 165 6 / 165
Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion BDI Architecture Programming Languages for Cognitive Agents Programming Languages for Multi-Agent Systems E.g., Jason, Jadex, JACK, 2APL, GOAL, Brahms, JIAC, Agent Intentional Stance (Dennett) Factory, MetateM, Golog variants, ... Practical Reasoning (Bratman) IRMA (Bratman, Isreal, Pollack) Architecture to represent an agent mental state: PRS (George↵, Lansky) Beliefs: information available to agent (e.g., about the dMARS (Kinny) environment or other agents) Goals: states of a↵airs that the agent wants to achieve BDI Logics and Agent Architecture (Rao, George↵) Events: changes in agents beliefs or goals Wooldridge, Singh, ... Capabilities: reusable modules of activities that the agent can perform Plans: reasoning about courses of action to achieve goals Rules: reasoning about beliefs 7 / 165 8 / 165 Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion Programming Languages for Cognitive Agents Programming Languages/Platforms for Organisations Concepts used to specify the state of an organisation: Some steps of a Reasoning Cycle: Agents, Roles, Groups Determining Relevant Plans for Handling Events Norms, Obligations, Prohibitions, Permissions, Violations Select a Plan for Execution Dependency, Power, Delegation, Information flow relations Execute Part of an Intended Plans Deadlines, Sanctions, Rewards Handle Plan Failures Management Infrastructure to control and coordinate agent behaviour at run-time: Agent Interpreter is an infinite loop of such reasoning cycles. Endogenous: The control is a part of the agent program The architecture and reasoning cycle together with the agent Exogenous: The control is performed by an external system program (specially plans) determine the behaviour of the agent. Monitoring Agent Behaviour Enforcing Organisational Rules Regimenting Organisational Rules 9 / 165 10 / 165 Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion Programming Languages/Platforms for Environments Hands-on Part of this Course We will use JaCaMo Artifacts to represent the state of the environment First fully operational, unified platform covering the 3 main Access to Databases/Services/etc., Coordination, Interaction levels of abstractions for multi-agent oriented programming Environment “objects”, i.e., non-proactive entities JaCaMo = Jason + CArtAgO + Moise Processing Operations on Artifacts http://jacamo.sourceforge.net Realising the e↵ects of environments actions http://jason.sourceforge.net Providing events related to sensing the environment http://cartago.sourceforge.net Synchronising agent actions http://moise.sourceforge.net At the right level of abstraction for a multi-agent system More than the sum of 3 successful platforms Revealing the full potential of Multi-Agent Oriented Programming 11 / 165 12 / 165
Outline Environment Oriented 2 Environment Programming Why Environment Programming in MAS Programming Basic Level Advanced Level — EOP — A&A and CArtAgO Conclusions and Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Back to the Notion of Environment in MAS Single Agent Perspective sensors The notion of environment is intrinsically related to the db ack pe fee PERCEPTION rce notion of agent and multi-agent system pts ENVIRONMENT “An agent is a computer system that is situated in some DECISION acti do environment and that is capable of autonomous action in ons tion to ac ACTION this environment in order to meet its design objective” [Wooldridge, 2002] effectors / actuators “An agent is anything that can be viewed as perceiving its environment through sensors and acting upon the environment through e↵ectors. Perception ” [Russell and Norvig, 2003] process inside agent inside of attaining awareness or Including both physical and software environments understanding sensory information, creating percepts perceived form of external stimuli or their absence Actions the means to a↵ect, change or inspect the environment 15 / 165 16 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Multi-Agent Perspective Why Environment Programming Basic level to create testbeds for real/external environments to ease the interface/interaction with existing software environments Advanced level to uniformly encapsulate and modularise functionalities of the MAS out of the agents typically related to interaction, coordination, organisation, security externalisation this implies changing the perspective on the environment environment as a first-class abstraction of the MAS In evidence endogenous environments (vs. exogenous ones) programmable environments overlapping spheres of visibility and influence ..which means: interaction 17 / 165 18 / 165
Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Environment Programming: General Issues Basic Level Overview MAS MAS ENVIRONMENT actions Defining the interface SIMULATED REAL WORLD mimicking actions, perceptions WORLD (PHYSICAL OR COMPUTATIONAL) data-model OR Defining the environment computational model & architecture how the environment works INTERFACE EXTERNAL WORLD structure, behaviour, topology (PHYSICAL OR core aspects to face: concurrency, distribution COMPUTATIONAL) OR Defining the environment programming model how to program the environment WRAPPER TO EXISTING Example: percepts JAVA TECHNOLOGY AGENTS PLATFORM 19 / 165 20 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Basic Level: Features An Example: Jason [Bordini et al., 2007] Flexible Java-based Environment API Environment conceptually conceived as a single monolitic Environment base class to be specialised block executeAction method to specify action semantics addPercept to generate percepts providing actions, generating percepts Environment API Environment User Agent -globalPercepts: List<Literal> Environment Architecture to define the set of actions and program actions -agPercepts: Map<String,List<Literal>> +init(String[] args) computational behaviour +stop() getPercepts which include the generation of percepts +getPercepts(String agName): List<Literal> +executeAction(String agName, Structure action): boolean typically implemented using as single object/class in OO such +addPercept(String agName, Literal p) as Java +removePercept(String agName, Literal p) ... method to execute actions executeAction fields to store the environment state UserEnvironment available in many agent programming languages/frameworks +init(String[] args) change +executeAction(String agName, Structure action): boolean e.g., Jason, 2APL, GOAL, JADEX percepts 21 / 165 22 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up MARS Environment in Jason Jason Agents Playing on Mars // mars robot 1 public class MarsEnv extends Environment { ... private MarsModel model; ... private MarsView view; /* creates the agents perception /* Initial beliefs */ * based on the MarsModel */ +!take(S,L) : true public void init(String[] args) { void updatePercepts() { at(P) :- pos(P,X,Y) & pos(r1,X,Y). <- !ensure_pick(S); model = new MarsModel(); !at(L); view = new MarsView(model); clearPercepts(); /* Initial goal */ drop(S). model.setView(view); updatePercepts(); Location r1Loc = model.getAgPos(0); !check(slots). +!ensure_pick(S) : garbage(r1) } Location r2Loc = model.getAgPos(1); <- pick(garb); /* Plans */ !ensure_pick(S). public boolean executeAction(String ag, Structure action) { Literal pos1 = Literal.parseLiteral +!ensure_pick(_). String func = action.getFunctor(); ("pos(r1," + r1Loc.x + "," + r1Loc.y + ")"); +!check(slots) : not garbage(r1) if (func.equals("next")) { Literal pos2 = Literal.parseLiteral <- next(slot); +!at(L) : at(L). model.nextSlot(); ("pos(r2," + r2Loc.x + "," + r2Loc.y + ")"); !!check(slots). +!at(L) <- ?pos(L,X,Y); } else if (func.equals("move_towards")) { int x = (int)((NumberTerm)action.getTerm(0)).solve(); addPercept(pos1); +!check(slots). move_towards(X,Y); int y = (int)((NumberTerm)action.getTerm(1)).solve(); addPercept(pos2); !at(L). model.moveTowards(x,y); +garbage(r1) : not .desire(carry_to(r2)) } else if (func.equals("pick")) { if (model.hasGarbage(r1Loc)) { <- !carry_to(r2). model.pickGarb(); addPercept(Literal.parseLiteral("garbage(r1)")); } else if (func.equals("drop")) { } +!carry_to(R) model.dropGarb(); <- // remember where to go back } else if (func.equals("burn")) { if (model.hasGarbage(r2Loc)) { ?pos(r1,X,Y); model.burnGarb(); addPercept(Literal.parseLiteral("garbage(r2)")); -+pos(last,X,Y); } else { } return false; } // carry garbage to r2 } !take(garb,R); class MarsModel extends GridWorldModel { ... } updatePercepts(); // goes back and continue to check return true; class MarsView extends GridWorldView { ... } !at(last); } } ... !!check(slots). ... 23 / 165 24 / 165
Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Another Example: 2APL [Dastani, 2008] Example: Block-world Environment in 2APL package blockworld; 2APL public class Env extends apapl.Environment { BDI-based agent-oriented programming language integrating declarative programming constructs (beliefs, goals) and public void enter(String agent, Term x, Term y, Term c){...} imperative style programming constructs (events, plans) Java-based Environment API public Term sensePosition(String agent){...} Environment base class public Term pickup(String agent){...} implementing actions as methods public void north(String agent){...} inside action methods external events can be generated to be perceived by agents as percepts ... } 25 / 165 26 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up 2APL Agents in the block-world Environment Interface Stardard – EIS Initiative Recent initiative supported by main APL research BeliefUpdates: ... { bomb(X,Y) } RemoveBomb(X,Y){ not bomb(X,Y) } groups [Behrens et al., 2010] { true } AddBomb(X,Y) { bomb(X,Y) } PC-rules: { carry(bomb) } { not carry(bomb) } Drop( ) PickUp( ) { { not carry(bomb)} carry(bomb) } goto( X, Y ) <- true | { GOAL, 2APL, GOAL, JADEX, JASON @blockworld( sensePosition(), POS ); Beliefs: B(POS = [A,B]); Goal of the initiative start(0,1). if B(A > X) then bomb(3,3). clean( blockWorld ) :- { @blockworld( west(), L ); goto( X, Y ) design and develop a generic environment interface standard not bomb(X,Y) , not carry(bomb). } else if B(A < X) then a standard to connect agents to environments Plans: B(start(X,Y)) ; { @blockworld( east(), L ); goto( X, Y ) ... environments such as agent testbeds, commercial @blockworld( enter( X, Y, blue ), L ) } applications, video games.. else if B(B > Y) then Goals: { @blockworld( north(), L ); clean( blockWorld ) goto( X, Y ) Principles } PG-rules: clean( blockWorld ) <- bomb( X, Y ) | else if B(B < Y) then { @blockworld( south(), L ); wrapping already existing environments { goto( X, Y ); } goto( X, Y ) creating new environments by connecting already existing @blockworld( pickup( ), L1 ); PickUp( ); } apps RemoveBomb( X, Y ); goto( 0, 0 ); ... creating new environments from scratch @blockworld( drop( ), L2 ); } Drop( ) Requirements ... generic reuse 27 / 165 28 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up EIS Meta-Model Environment Interface Features Interface functions attaching, detaching, and notifying observers (software design pattern); registering and unregistering agents; adding and removing entities; managing the agents-entities-relation; performing actions and retrieving percepts; managing the environment By means of the Env. Interface agents perform actions and Interface Intermediate language collect percepts to facilitate data-exchange actually actions/percepts are issued to controllable entities in encoding percepts, actions, events environment model represent the agent bodies, with e↵ectors and sensors 29 / 165 30 / 165
Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Advanced Level Overview Three Support Levels [Weyns et al., 2007] Vision: environment as a first-class abstraction in MAS [Weyns et al., 2007, Ricci et al., 2010b] application or endogenous environments, i.e. that environment which is an explicit part of the MAS Basic interface support providing an exploitable design & programming abstraction Abstraction support level to build MAS applications Interaction-mediation support level Outcome distinguishing clearly between the responsibilities of agent and environment separation of concerns improving the engineering practice 31 / 165 32 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Basic Interface Support Abstraction Support Bridges the conceptual gap between the agent abstraction The environment enables agents to access the deployment and low-level details of the deployment context context shields low-level details of the deployment context i.e. the hardware and software and external resources with which the MAS interacts 33 / 165 34 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Interaction-Mediation Support Environment Definition Revised Regulate the access to shared resources Mediate interaction between agents Environment definition revised [Weyns et al., 2007] The environment is a first-class abstraction that provides the surrounding conditions for agents to exist and that mediates both the interaction among agents and the access to resources 35 / 165 36 / 165
Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Research on Environments for MAS Environment Programming Environment as first-class programming Environments for Multi-Agent Systems research field / abstraction [Ricci et al., 2010b] E4MAS workshop series [Weyns et al., 2005] software designers and engineers perspective di↵erent themes and issues (see JAAMAS Special endogenous environments (vs. exogenous one) Issue [Weyns and Parunak, 2007] for a good survey) programming MAS = mechanisms, architectures, infrastructures, programming Agents + programming Environment applications [Platon et al., 2007, Weyns and Holvoet, 2007, Weyns and Holvoet, 2004, Viroli et al., 2007] ..but this will be extended to include OOP in next part the main perspective is (agent-oriented) software engineering Environment as first-class runtime abstraction for agents Focus of this tutorial: the role of the environment agent perspective abstraction in MAS programming to be observed, used, adapted, constructed, ... environment programming Defining computational and programming frameworks/models also for the environment part 37 / 165 38 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Computational Frameworks for Environment Programming Models for the Environment: Desiderata Programming: Issues Abstraction keeping the agent abstraction level e.g. no agents sharing and Defining the environment interface calling OO objects actions, percepts, data model e↵ective programming models for controllable and observable contract concept, as defined in software engineering contexts computational entities (Design by Contract) Modularity Defining the environment computational model away from the monolithic and centralised view environment structure, behaviour Orthogonality Defining the environment distribution model wrt agent models, architectures, platforms topology support for heterogeneous systems 39 / 165 40 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Programming Models for the Environment: Desiderata Existing Computational Frameworks AGRE / AGREEN / MASQ [Stratulat et al., 2009] AGRE – integrating the AGR (Agent-Group-Role) organisation model with a notion of environment Environment used to represent both the physical and social part of interaction Dynamic extensibility AGREEN / MASQ – extending AGRE towards a unified dynamic construction, replacement, extension of environment representation for physical, social and institutional parts environments support for open systems Based on MadKit platform [Gutknecht and Ferber, 2000a] Reusability GOLEM [Bromuri and Stathis, 2008] reuse of environment parts for di↵erent kinds of applications Logic-based framework to represent environments for situated cognitive agents composite structure containing the interaction between cognitive agents and objects A&A and CArtAgO [Ricci et al., 2010b] introducing a computational notion of artifact to design and implement agent environments 41 / 165 42 / 165
Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Agents and Artifacts (A&A) Conceptual Model: Background Human Metaphor CLOCK WHITEBOARD BAKERY artifact artifact workspace agents can join dynamically the workspace A&A and CArtAgO ARCHIVE artifact COM. CHANNEL artifact RESOURCE artifact TASK SCHEDULER artifact 44 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up A&A Basic Concepts [Omicini et al., 2008] A&A Programming Model Features [Ricci et al., 2007b] Agents Abstraction autonomous, goal-oriented pro-active entities create and co-use artifacts for supporting their activities artifacts as first-class resources and tools for agents besides direct communication Modularisation Artifacts artifacts as modules encapsulating functionalities, organized non-autonomous, function-oriented, stateful entities in workspaces controllable and observable Extensibility and openness modelling the tools and resources used by agents artifacts can be created and destroyed at runtime by agents designed by MAS programmers Reusability Workspaces artifacts (types) as reusable entities, for setting up di↵erent grouping agents & artifacts kinds of environments defining the topology of the computational environment 45 / 165 46 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up A&A Meta-Model in More Detail [Ricci et al., 2010b] Artifact Abstract Representation Manual consult SIGNALS has ObsPropName(Args) create OBSERVABLE Workspace Artifact dispose ... PROPERTIES link USAGE link INTERFACE OperationX(Params) Operation use Agent Environment ... generate OPERATIONS update Observable perceive Event OperationY(Params) Observable perceive ... Property observe LINK join INTERFACE quit 47 / 165 48 / 165
Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up A World of Artifacts A Simple Taxonomy Individual or personal artifacts designed to provide functionalities for a single agent use state available n_records 1001 e.g. an agenda for managing deadlines, a library... count 5 table_names ... wsdl ... inc state true ... ... Social artifacts reset switch GetLastTradePrice createTable ... addRecord query designed to provide functionalities for structuring and a flag a counter a Stock Quote Web Service ... managing the interaction in a MAS a data-base coordination artifacts [Omicini et al., 2004], organisation artifacts, ... n_items 0 next_todo check_plant max_items 100 last_todo ... clearEvents out e.g. a blackboard, a game-board,... postEvent in put setTodo get cancelTodo registerForEvs rd Boundary artifacts a bounded buffer an agenda an event service a tuple space to represent external resources/services e.g. a printer, a Web Service to represent devices enabling I/O with users e.g GUI, console, etc. 49 / 165 50 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Actions and Percepts in Artifact-Based Environments Interaction Model: Use Explicit semantics defined by the (endogenous) environment [Ricci et al., 2010c] success/failure semantics, execution semantics defining the contract (in the SE acceptation) provided by the PropName Value PropName Value environment ... op(parms) ... action actions ! artifacts’ operation op(Params) AGENT the action repertoire is given by the dynamic set of operations provided by the overall set of artifacts available in the workspace can be changed by creating/disposing artifacts action success/failure semantics is defined by operation semantics Performing an action corresponds to triggering the execution of an operation percepts ! artifacts’ observable properties + signals acting on artifact’s usage interface properties represent percepts about the state of the environment signals represent percepts concerning events signalled by the environment 51 / 165 52 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Interaction Model: Operation execution Interaction Model: Observation SIGNALS PropName Value OBS PROPERTIES CHANGE Value ... op(parms) ... PropName(Value). action PropName(Value). op(Params) ... PropName Value AGENT OPERATION EXECUTION PropName Value Belief base ... action completion (or alike) focus - with success or failure - AGENT OBSERVER a process structured in one or multiple transactional steps asynchronous with respect to agent ...which can proceed possibly reacting to percepts and Agents can dynamically select which artifacts to observe executing actions of other plans/activities predefined focus/stopFocus actions operation completion causes action completion action completion events with success or failure, possibly with action feedbacks 53 / 165 54 / 165
Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Interaction Model: Observation Artifact Linkability PropName(Value). PropName(Value). ... PropName Value PropName Value Belief base ... (or alike) use linkedOp AGENT OBSERVER WSP-X WSP-Y By focussing an artifact Basic mechanism to enable inter-artifact interaction observable properties are mapped into agent dynamic linking artifacts through interfaces (link interfaces) knowledge about the state of the world, as percepts operations triggered by an artifact over an other artifact e.g. belief base Useful to design & program distributed environments signals are mapped as percepts related to observable events realised by set of artifacts linked together possibly hosted in di↵erent workspaces 55 / 165 56 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Artifact Manual CArtAgO Common ARtifact infrastructure for AGent Open Agent-readable description of artifact’s... environment (CArtAgO) [Ricci et al., 2009b] ...functionality what functions/services artifacts of that type provide Computational framework / infrastructure to implement and ...operating instructions run artifact-based environment [Ricci et al., 2007c] how to use artifacts of that type Java-based programming model for defining artifacts set of basic API for agent platforms to work within Towards advanced use of artifacts by intelligent artifact-based environment agents [Piunti et al., 2008] dynamically choosing which artifacts to use to accomplish Distributed and open MAS their tasks and how to use them workspaces distributed on Internet nodes strong link with Semantic Web research issues agents can join and work in multiple workspace at a time Work in progress Role-Based Access Control (RBAC) security model defining ontologies and languages for describing the manuals Open-source technology available at http://cartago.sourceforge.net 57 / 165 58 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Integration with Agent Languages and Platforms JaCa Platform Integration of CArtAgO with Jason language/platform a JaCa program is a dynamic set of Jason agents working Integration with existing agent platforms [Ricci et al., 2008] together in one or multiple CArtAgO workspaces by means of bridges creating an action/perception interface and doing data binding Mapping actions Outcome Jason agent external actions are mapped onto artifacts’ developing open and heterogenous MAS operations introducing a further perspective on interoperability besides percepts the ACL’s one artifacts’ observable properties are mapped onto agent beliefs sharing and working in a common work environment artifacts’ signals are mapped as percepts related to common object-oriented data-model observable events data-model Jason data-model is extended to manage also (Java) objects 59 / 165 60 / 165
Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Example 1: A Simple Counter Artifact Example 1: User and Observer Agents class Counter extends Artifact { USER(S) OBSERVER(S) void init(){ defineObsProp("count",0); count 5 !create_and_use. !observe. } +!create_and_use : true +!observe : true @OPERATION void inc(){ <- !setupTool(Id); <- ?myTool(C); // discover the tool // use focus(C). ObsProperty p = getObsProperty("count"); inc inc; p.updateValue(p.intValue() + 1); // second use specifying the Id +count(V) signal("tick"); inc [artifact_id(Id)]. <- println(“observed new value: “,V). } } // create the tool +tick [artifact_name(Id,”c0”)] +!setupTool(C): true <- println(“perceived a tick”). <- makeArtifact("c0","Counter",C). +?myTool(CounterId): true <- lookupArtifact(“c0”,CounterId). Some API spots -?myTool(CounterId): true <- .wait(10); Artifact base class ?myTool(CounterId). @OPERATION annotation to mark artifact?s operations set of primitives to work define/update/.. observable properties signal primitive to generate signals Working with the shared counter 61 / 165 62 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Pre-defined Artifacts Example 2: Coordination Artifacts – A Bounded Bu↵er Each workspace contains by default a predefined set of public class BoundedBuffer extends Artifact { private LinkedList<Item> items; private int nmax; artifacts void init(int nmax){ items = new LinkedList<Item>(); providing core and auxiliary functionalities defineObsProperty("n_items",0); this.nmax = nmax; i.e. a pre-defined repertoire of actions available to agents... } n_items 5 @OPERATION void put(Item obj){ Among the others await("bufferNotFull"); items.add(obj); getObsProperty("n_items").updateValue(items.size()); put workspace, type: cartago.WorkspaceArtifact } get functionalities to manage the workspace, including security @OPERATION void get(OpFeedbackParam<Item> res) { await("itemAvailable"); Item item = items.removeFirst(); operations: makeArtifact, lookupArtifact, focus,... res.set(item); getObsProperty("n_items").updateValue(items.size()); node, type: cartago.NodeArtifact } @GUARD boolean itemAvailable(){ return items.size() > 0; } core functionalities related to a node @GUARD boolean bufferNotFull(Item obj){ return items.size() < nmax; } operations: createWorkspace, joinWorkspace, ... } console, type cartago.tools.Console operations: println,... Basic operation features blackboard, type cartago.tools.TupleSpace output parameters to represent action feedbacks operations: out, in, rd, ... long-term operations, with a high-level support for .... synchronization (await primitive, guards) 63 / 165 64 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Example 2: Producers and Consumers Remarks PRODUCERS CONSUMERS item_to_produce(0). !consume. !produce. +!consume: true +!produce: true <- ?bufferReady; <- !setupTools(Buffer); !consumeItems. Process-based operation execution semantics !produceItems. +!consumeItems: true action/operation execution can be long-term +!produceItems : true <- get(Item); <- ?nextItemToProduce(Item); !consumeItem(Item); action/operation execution can overlap put(Item); !!consumeItems. !!produceItems. key feature for implementing coordination functionalities +!consumeItem(Item) : true +?nextItemToProduce(N) : true <- .my_name(Me); Operation with output parameters as action feedbacks <- -item_to_produce(N); println(Me,": ",Item). +item_to_produce(N+1). +?bufferReady : true +!setupTools(Buffer) : true <- lookupArtifact("myBuffer",_). <- makeArtifact("myBuffer","BoundedBuffer", -?bufferReady : true [10],Buffer). <-.wait(50); ?bufferReady. -!setupTools(Buffer) : true <- lookupArtifact("myBuffer",Buffer). 65 / 165 66 / 165
Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Action Execution & Blocking Behaviour Example 3: Internal Processes – A Clock CLOCK CLOCK USER AGENT public class Clock extends Artifact { !test_clock. Given the action/operation map, by executing an action the boolean working; final static long TICK_TIME = 100; +!test_clock <- makeArtifac("myClock","Clock",[],Id); focus(Id); intention/activity is suspended until the corresponding void init(){ working = false; } +n_ticks(0); start; operation has completed or failed @OPERATION void start(){ if (!working){ working = true; println("clock started."). @plan1 action completion events generated by the environment and execInternalOp("work"); } else { +tick: n_ticks(10) <- stop; failed("already_working"); println("clock stopped."). automatically processed by the agent/environment platform } } @plan2 [atomic] bridge @OPERATION void stop(){ working = false; } +tick: n_ticks(N) <- -+n_ticks(N+1); println("tick perceived!"). no need of explicit observation and reasoning by agents to @INTERNAL_OPERATION void work(){ while (working){ know if an action succeeded signal("tick"); await_time(TICK_TIME); } However the agent execution cycle is not blocked! } } the agent can continue to process percepts and possibly execute actions of other intentions Internal operations execution of operations triggered by other operations implementing controllable processes 67 / 165 68 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Example 4: Artifacts for User I/O – GUI Artifacts Example 4: Agent and User Interaction GUI ARTIFACT USER ASSISTANT AGENT public class MySimpleGUI extends GUIArtifact { !test_gui. private MyFrame frame; +!test_gui public void setup() { <- makeArtifact("gui","MySimpleGUI",Id); frame = new MyFrame(); focus(Id). linkActionEventToOp(frame.okButton,"ok"); +value(V) linkKeyStrokeToOp(frame.text,"ENTER","updateText"); <- println("Value updated: ",V). value 16.0 ok linkWindowClosingEventToOp(frame, "closed"); defineObsProperty("value",getValue()); +ok : value(V) frame.setVisible(true); <- setValue(V+1). } setValue closed +closed @INTERNAL_OPERATION void ok(ActionEvent ev){ <- .my_name(Me); user signal("ok"); .kill_agent(Me). } agent @OPERATION void setValue(double value){ frame.setText(""+value); updateObsProperty("value",value); } ... Exploiting artifacts to enable interaction between human @INTERNAL_OPERATION void updateText(ActionEvent ev){ updateObsProperty("value",getValue()); users and agents } private int getValue(){ return Integer.parseInt(frame.getText()); } class MyFrame extends JFrame {...} } 69 / 165 70 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Other Features A&A and CArtAgO: Some Research Explorations Designing and implementing artifact-based organisation Other CArtAgO features not discussed in this lecture Infrastructures linkability JaCaMo model and platform (which is the evolution of the executing chains of operations across multiple artifacts ORA4MAS infrastructure [H¨bner et al., 2009c]) u multiple workspaces Cognitive stigmergy based on artifact agents can join and work in multiple workspaces, concurrently environments [Ricci et al., 2007a] including remote workspaces cognitive artifacts for knowledge representation and RBAC security model coordination [Piunti and Ricci, 2009] workspace artifact provides operations to set/change the Artifact-based environments for access control policies of the workspace, depending on the argumentation [Oliva et al., 2010] agent role ruling agents’ access and use of artifacts of the workspace Including A&A in AOSE methodology [Molesini et al., 2005] ... Defining a Semantic (OWL-based) description of artifact See CArtAgO papers and manuals for more information environments ( CArtAgO-DL) JaSa project = JASDL + CArtAgO-DL ... 71 / 165 72 / 165
Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Applying CArtAgO and JaCa Wrap-up Environment programming Using CArtAgO/JaCa for building real-world applications and environment as a programmable part of the MAS infrastructures encapsulating and modularising functionalities useful for Some examples agents’ work JaCa-Android Artifact-based environments implementing mobile computing applications on top of the artifacts as first-class abstraction to design and program Android platform using JaCa [Santi et al., 2011] complex software environments http://jaca-android.sourceforge.net usage interface, observable properties / events, linkability JaCa-WS / CArtAgO-WS artifacts as first-order entities for agents building SOA/Web Services applications using interaction based on use and observation JaCa [Ricci et al., 2010a] agents dynamically co-constructing, evolving, adapting their http://cartagows.sourceforge.net world JaCa-Web CArtAgO computational framework implementing Web 2.0 applications using JaCa programming and executing artifact-based environments http://jaca-web.sourceforge.net integration with heterogeneous agent platforms JaCa case 73 / 165 74 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Abstractions in Multi-Agent Systems schema mission role ORGAMISATION org LEVEL Organisation Oriented agent Programming AGENT LEVEL — OOP — artifact ENDOGENOUS ENVIRONMENT wsp LEVEL EXOGENOUS ENVIRONMENT 76 / 165 network node Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation in MAS – a definition Outline 3 Organisation Oriented Programming (OOP) What is an organisation? Fundamentals Motivations Some OOP approaches Focus on the Moise framework Moise Organisation Modelling Language (OML) Moise Organisation Management Infrastructure (OMI) Moise integration with agents & environment 78 / 165
Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation in MAS – a definition Introduction: Some definitions Organisations are structured, patterned systems of activity, knowledge, culture, memory, history, and capabilities that are distinct from any single agent [Gasser, 2001] What is an organisation? Organisations are supra-individual phenomena A decision and communication schema which is applied to a set of Pattern of agent cooperation actors that together fulfill a set of tasks in order to satisfy goals with a purpose while guarantying a global coherent state [Malone, 1999] supra-agent definition by the designer, or by actors, to achieve a purpose emergent or An organisation is characterised by: a division of tasks, a predefined (by designer or agents) distribution of roles, authority systems, communication systems, contribution-retribution systems [Bernoux, 1985] pattern of predefined cooperation An arrangement of relationships between components, which results into an entity, a system, that has unknown skills at the level of the individuals [Morin, 1977] pattern of emergent cooperation 78 / 165 79 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Perspective on organisations from EASSS’05 Tutorial (Sichman, Boissier) Perspective on organisations from EASSS’05 Tutorial (Sichman, Boissier) Agent Centred Agent Centred Swarms, AMAS, SASO Social Reasoning Self-organisations … Coalition formation Contract Net Protocol … Organisation is observed. Organisation is observed. Implicitly programmed Coalition formation in Agents, Interactions, mechanisms programmed Environment. in Agents. Agents don’t know Agents know Agents don’t know Agents know about organisation about organisation about organisation about organisation AOSE TAEMS, STEAM, AGR MASE, GAIA, MESSAGE, … MOISE+, OPERA, … Organisation is Organisation-Oriented a design model. Programming of MAS It is hard-coded in Agents Organisation Centred Organisation Centred Organisation Specification Designer / Observer Organisation Specification Designer / Observer Organisation Entity Observed Organisation Bottom-up Top-down Organisation Entity Observed Organisation Bottom-up Top-down 80 / 165 81 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation Oriented Programming (OOP) Organisation Oriented Programming (OOP) Programming outside the agents Agent Agent Using organisational concepts Agent To define a cooperative Agent First approach Organisation Organisation Specification pattern Specification Agents read the program and follow it Agent Program = Specification Agent By changing the Organisation specification, we can Organisation Entity change the MAS overall Entity behaviour 82 / 165 82 / 165
Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation Oriented Programming (OOP) Organisation Oriented Programming (OOP) Agent Agent Second approach Second approach Agent Agents are forced to Agent Agents are forced to Organisation follow the program Organisation follow the program Specification Specification Agents are rewarded if Agent Agent they follow the program ... Organisation Organisation Entity Entity 82 / 165 82 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation Oriented Programming (OOP) Components of OOP: Organisation Modelling Language (OML) Agent Declarative specification of the organisation(s) Components Specific constraints, norms and cooperation patterns Programming language imposed on the agents Agent (OML) Organisation Based on an organisational model Specification Platform (OMI) e.g. AGR [Ferber and Gutknecht, 1998], Integration to agent TeamCore [Tambe, 1997], Agent architectures and Islander [Esteva et al., 2001], environment Moise+ [H¨bner et al., 2002], u Organisation Opera [Dignum and Aldewereld, 2010], Entity 2OPL [Dastani et al., 2009a], ... 82 / 165 83 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Components of OOP: Components of OOP: Organisation Management Infrastructure (OMI) Integration mechanisms Agent integration mechanisms allow agents to be aware of and to deliberate on: Coordination mechanisms, i.e. support infrastructure entering/exiting the organisation e.g. MadKit [Gutknecht and Ferber, 2000b], modification of the organisation Karma [Pynadath and Tambe, 2003], obedience/violation of norms ... sanctioning/rewarding other agents Regulation mechanisms, i.e. governance infrastructure e.g. J -Moise+ [H¨bner et al., 2007], Autonomy based u e.g. Ameli [Esteva et al., 2004], reasoning [Carabelea, 2007], ProsA2 Agent-based reasoning S-Moise+ [H¨bner et al., 2006], u on norms [Ossowski, 1999], ... ORA4MAS [H¨bner et al., 2009b], u Environment integration mechanisms ... transform organisation into embodied organisation so that: Adaptation mechanisms, i.e. reorganisation infrastructure organisation may act on the environment (e.g. enact rules, regimentation) environment may act on the organisation (e.g. count-as rules) e.g [Piunti et al., 2009b], [Okuyama et al., 2008] 84 / 165 85 / 165
Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Motivations for OOP: Motivations for OOP: Applications point of view Constitutive point of view Current applications show an increase in Organisation helps the agents to cooperate with other Number of agents agents by defining common cooperation schemes Duration and repetitiveness of agent activities global tasks Heterogeneity of the agents protocols Number of designers of agents groups, responsibilities Agent ability to act and decide e.g. ‘to bid’ for a product on eBay is an institutional action only Openness, scalability, dynamism possible because eBay defines rules for that very action More and more applications require the integration of human the bid protocol is a constraint but it also creates the action communities and technological communities (ubiquitous and pervasive computing), building connected communities e.g. when a soccer team wants to play match, the organisation (ICities) in which agents act on behalf of users helps the members of the team to synchronise actions, to Trust, security, ..., flexibility, adaptation share information, etc 86 / 165 87 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Motivations for OOP: Motivations for OOP: Normative point of view Agents point of view MAS have two properties which seem contradictory: a global purpose autonomous agents An organisational specification is required to enable agents to While the autonomy of the agents is essential, it may cause “reason” about the organisation: loss in the global coherence of the system and achievement of to decide to enter into/leave from the organisation during the global purpose execution Embedding norms within the organisation of an MAS is a Organisation is no more closed way to constrain the agents’ behaviour towards the global to change/adapt the current organisation purposes of the organisation, while explicitly addressing the Organisation is no more static autonomy of the agents within the organisation Normative organisation to obey/disobey the organisation e.g. when an agent adopts a role, it adopts a set of behavioural Organisation is no more a regimentation constraints that support the global purpose of the organisation. It may decide to obey or disobey these constraints 88 / 165 89 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Motivations for OOP: Some OOP approaches Organisation point of view An organisational specification is required to enable the organisation to “reason” about itself and about the agents in AGR/Madkit [Ferber and Gutknecht, 1998] order to ensure the achievement of its global purpose: STEAM/Teamcore [Tambe, 1997] to decide to let agents enter into/leave from the ISLANDER/AMELI [Esteva et al., 2004] organisation during execution Opera/Operetta [Dignum and Aldewereld, 2010] Organisation is no more closed to decide to let agents change/adapt the current PopOrg [Rocha Costa and Dimuro, 2009] organisation 2OPL [Dastani et al., 2009a] Organisation is no more static and blind ... to govern agents behaviour in the organisation (i.e. monitor, enforce, regiment) Organisation is no more a regimentation 90 / 165 91 / 165
Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise AGR [Ferber and Gutknecht, 1998] AGR OML is member of * Agent * Agent Group Role (previously known as AALAADIN) plays 1..* Agent: Active entity that plays roles within groups. An agent Group Agent may have several roles and may belong to several groups level Group: set of agents sharing common characteristics, i.e. 1 context for a set of activities. Two agents cant communicate described by Organization with each other if they dont belong to the same group 1 1..* 1 contains level Role: Abstract representation of the status, position, 1..* participant Group structure Role 1..* function of an agent within a group 1 1 initiator source 1 1 target OMI: the Madkit platform * 1 1 * Interaction Role properties Role dependency protocol * 92 / 165 93 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise AGR OML Modelling Dimensions STEAM [Tambe, 1997] Environment Shell for TEAMwork is a general framework to enable agents to participate in teamwork P Di↵erent applications: Attack, Transport, Robocup soccer E Based on an enhanced SOAR architecture and 300 domain OS independent SOAR rules Principles: Team synchronisation: Establish joint intentions, Monitor team progress and repair, Individual may fail or succeed in Structural Specification B own role Reorganise if there is a critical role failure B: agents’ possible behaviors Reassign critical roles based on joint intentions P: agents’ behaviors that lead to global purpose Decision theoretic communication E: agents’ possible behaviors constrained by the environment OS: agents’ possible behaviors structurally constrained by the organization Supported by the TEAMCORE OMI 94 / 165 95 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise STEAM OML [Tambe, 1997] STEAM OML Modelling Dimensions TASK FORCE EVACUATE Environment [TASK FORCE] P PROCESS ORDERS EXECUTE LANDING ZONE E ORDERS SAFETY INFO FLIGHT ROUTE MISSION [TASK FORCE] [TASK FORCE] MANEUVERS OBTAINER OBTAINER TEAM PLANNER [TASK FORCE] OS ESCORT TRANSPORT OBTAIN FLY-FLIGHT MASK PICKUP ORDERS PLAN OBSERVE [TRANSPORT] [ORDERS [TASK FORCE] [ESCORT] OBTAINER] Structural OF Functional HELO1 HELO2 HELO1 HELO2 Specification B Specification FLY-CONTROL ROUTE [TASK FORCE] B: agents’ possible behaviors Organization: hierarchy of roles that P: agents’ behaviors that lead to global purpose may be filled by agents or groups of Team Plan: E: agents’ possible behaviors constrained by the environment agents. •  initial conditions, OS: agents’ possible behaviors structurally constrained by the organization •  term. cond. : achievability, irrelevance, OF: agents’ possible behaviors functionally constrained by the organization unachievability •  team-level actions. 96 / 165 97 / 165
Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise ISLANDER ISLANDER OML: IDL [Esteva et al., 2001] (define-institution soccer-server as Based on di↵erent influences: economics, norms, dialogues, dialogic-framework = soccer-df coordination performative-structure = soccer-pf norms = ( free-kick coach-messages … ) electronic institutions ) Combining di↵erent alternative views: dialogical, normative, coordination Institution Description Language: Performative structure (Network of protocols) Scene (multi-agent protocol) Roles Norms Ameli as OMI Performative Structure 98 / 165 99 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise ISLANDER OML Modelling Dimensions 2OPL slides from Dastani Environment The aim is to design and develop a programming language to P E support the implementation of coordination mechanisms in terms of normative concepts. OS An organisation OI determines e↵ect of external actions Structural Dialogical Specification B Specification normatively assesses e↵ect of agents’ actions (monitoring) B: agents’ possible behaviors sanctions agents’ wrongdoings (enforcement) P: agents’ behaviors that lead to global purpose prevents ending up in really bad states (regimentation) E: agents’ possible behaviors constrained by the environment OS: agents’ possible/permitted/obliged behaviors structurally constrained by the organisation OI: agents’ possible/permitted/obliged behaviors interactionally constrained by the organisation 100 / 165 101 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Programming Language for Organisations 2OPL Modelling Dimension Example (Train Station) Facts: – -at˙platform , -in˙train , -ticket ˝ Effects: – -at˙platform ˝ enter – at˙platform ˝, – -ticket ˝ buy˙ticket – ticket ˝, – at˙platform , -in˙train ˝ embark – -at˙platform, in˙train ˝ Counts˙as rules: – at˙platform , -ticket ˝ =¿ – viol˙ticket ˝, – in˙train , -ticket ˝ =¿ – viol˙—˙ ˝ Sanction˙rules: – viol˙ticket ˝ =¿ – fined˙10 ˝ 102 / 165 103 / 165
Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Summary Several models Several dimensions on modelling organisation The Moise Framework Structural (roles, groups, ...) Functional (global plans, ....) Dialogical (scenes, protocols, ...) Normative (norms) 104 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Moise Framework Moise Modelling Dimensions OML (language) Environment Tag-based language (issued from Moise [Hannoun et al., 2000], P Moise+ [H¨bner et al., 2002], u E MoiseInst [Gˆteau et al., 2005]) a OMI (infrastructure) OS developed as an artifact-based working environment (ORA4MAS [H¨bner et al., 2009b] based on CArtAgO u nodes, refactoring of S-Moise+ [H¨bner et al., 2006] and u Structural OF Functional Specification B Specification Synai [Gˆteau et al., 2005]) a Integrations Groups, links, roles Global goals, plans, Compatibilities, multiplicities Missions, schemas, Agents and Environment (c4Jason, c4Jadex Normative Specification preferences inheritance [Ricci et al., 2009c]) Permissions, Obligations Environment and Organisation ([Piunti et al., 2009a]) Allows agents autonomy! Agents and Organisation (J -Moise+ [H¨bner et al., 2007]) u 106 / 165 107 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Moise OML meta-model (partial view) Moise OML Agent Goal OML for defining organisation specification and organisation entity Structural Specification create delete Three independent dimensions [H¨bner et al., 2007] u create delete ( well adapted for the reorganisation concerns): Normative Social Scheme Specification Structural: Roles, Groups Group achieve Functional Specification Goal Functional: Goals, Missions, Schemes adopt leave Normative: Norms (obligations, permissions, interdictions) commit Role Norm Mission leave Abstract description of the organisation for the designers the agents composition agent's actions J -Moise+ [H¨bner et al., 2007] u association concept mapping the Organisation Management Infrastructure Cardinalities are not represented ORA4MAS [H¨bner et al., 2009b] u 108 / 165 109 / 165
Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Moise OML Structural Specification Structural Specification Example Specifies the structure of an MAS along three levels: Individual with Role Social with Link Collective with Group Components: Role: label used to assign rights and constraints on the behavior of agents playing it Link: relation between roles that directly constrains the agents in their interaction with the other agents playing the corresponding roles Group: set of links, roles, compatibility relations used to define a shared context for agents playing roles in it Graphical representation of structural specification of 3-5-2 Joj Team 110 / 165 111 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Moise OML Functional Specification Functional Specification Example Specifies the expected behaviour of an MAS in terms of goals along two levels: m1, m2, m3 score a goal Collective with Scheme Individual with Mission m1 m3 Components: get the ball shot at the opponent’s goal Goals: m1 m2 Achievement goal (default type). Goals of this type should go towards the opponent field kick the ball to the goal area be declared as satisfied by the agents committed to them, m2 m2 be placed in the middle field go to the opponent back line when achieved m1 Maintenance goal. Goals of this type are not satisfied at a m3 kick the ball to (agent committed to m2) precise moment but are pursued while the scheme is running. be placed in the opponent goal area The agents committed to them do not need to declare that Key Organizational Entity they are satisfied Scheme Lucio m1 Scheme: global goal decomposition tree assigned to a group missions Cafu m2 goal sequence choice parallelism Any scheme has a root goal that is decomposed into subgoals success rate Rivaldo m3 Missions: set of coherent goals assigned to roles within Graphical representation of social scheme “side attack” for joj team norms 112 / 165 113 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Goal States Moise OML Normative Specification waiting enabled Explicit relation between the functional and structural specifications impossible satisfied Permissions and obligations to commit to missions in the context of a role Makes explicit the normative dimension of a role waiting initial state enabled goal pre-conditions are satisfied & scheme is well-formed satisfied agents committed to the goal have achieved it impossible the goal is impossible to be satisfied 114 / 165 115 / 165
Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Norm Specification – example Organisational Entity structural norrmative functional groups schemas Organisation links norms roles missions specification role deontic mission TTF back obliged m1 get the ball, go ... 1 minute purpose left obliged m2 be placed at ..., kick ... 3 minute right obliged m2 1 day attacker obliged m3 kick to the goal, ... 30 seconds group schema instances role instances mission Organisation player player Entity agents 116 / 165 117 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation Entity Dynamics Organisation management infrastructure (OMI) Responsibility 1 Organisation is created (by the agents) Managing – coordination, regulation – the agents’ execution instances of groups within organisation defined in an organisational specification instances of schemes 2 Agents enter into groups adopting roles 3 Groups become responsible for schemes Agent Agent Agent Agent Agents from the group are then obliged to commit to missions in the scheme 4 Agents commit to missions 5 Agents fulfil mission’s goals Organisation OMI Specification 6 Agents leave schemes and groups 7 Schemes and groups instances are destroyed (e.g. MadKit, AMELI, S-Moise+ , ...) 118 / 165 119 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisational artifacts in ORA4MAS ORA4MAS – GroupBoard artifact System GroupBoard based on A&A and Agent Moise Specification Observable Properties: Agent agents create and handle Players specification: the organisational artifacts specification of the group in Agent Agent Schemes the OS (an object of class Group artifacts in charge of Artifact Formation status moise.os.ss.Group) regimentations, detection and evaluation players: a list of agents Scheme of norms compliance playing roles in the group. Artifact adoptRole Each element of the list is a agents are in charge of leaveRole pair (agent x role) Scheme Agent decisions about Artifact Agent sanctions addScheme schemes: a list of scheme identifiers that the group is distributed solution removeScheme responsible for 120 / 165 121 / 165
Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise ORA4MAS – GroupBoard artifact ORA4MAS – SchemeBoard artifact GroupBoard SchemeBoard Observable Properties: Specification Operations: Specification specification: the specification of the scheme adoptRole(role): the agent Groups Players in the OS executing this operation Schemes tries to adopt a role in the Players groups: a list of groups group Goals responsible for the scheme Formation status leaveRole(role) Obligations players: a list of agents committed to the scheme. addScheme(schid): the adoptRole Each element of the list is a group starts to be commitMission pair (agent, mission) leaveRole responsible for the scheme leaveMission goals: a list with the managed by the addScheme goalAchieved current state of the goals SchemeBoard schId removeScheme removeScheme(schid) setGoalArgument obligations: list of obligations currently active in the scheme 122 / 165 123 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise ORA4MAS – SchemeBoard artifact Organisational Artifact Architecture SchemeBoard Organisation Operations: Group State Artifact Specification commitMission(mission) Groups NPL Interpreter and leaveMission: NPL Engine Players operations to “enter” and NOPL translates Organisation Program Specificatoin Goals “leave” the scheme Obligations goalAchieved(goal): defines State Obligations that some goal is achieved by the agent performing the commitMission operation Signals (o = obligation(to whom, reason, what, deadline)): leaveMission setGoalArgument(goal, obl created(o): the obligation o is created goalAchieved argument, value): defines obl fulfilled(o): the obligation o is fulfilled the value of some goal’s obl unfulfilled(o): the obligation o is unfulfilled setGoalArgument argument obl inactive(o): the obligation o is inactive norm failure(f ): the failure f has happened 124 / 165 125 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Environment integration Environment integration: constitutive rules Count-As rule Organisational Artifacts enable organisation and environment An event occurring on an artifact, in a particular context, may integration count-as an institutional event Embodied organisation [Piunti et al., 2009a] transforms the events created in the working environment into activation of an organisational operation count-as indirect automatic updating of the organisation count-as Enact rule Env. Artifact Org. Artifact An event produced on an organisational artifact, in a specific institutional context, may “enact” change and updating of the working environment (i.e., to promote equilibrium, avoid enact undesiderable states) Installing automated control on the working environment Even without the intervention of organisational/sta↵ agents status: ongoing work (regimenting actions on physical artifacts, enforcing 126 / 165 sanctions, ...) 127 / 165
Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Agent integration J -Moise: Jason + Moise Agents can interact with organisational artifacts as with Agents are programmed with Jason ordinary artifacts by perception and action BDI agents (reactive planning) – suitable abstraction level Any Agent Programming Language integrated with CArtAgO can use organisational artifacts The programmer has the possibility to express sophisticated recipes for adopting roles, committing to missions, fulfilling/violating norms, ... Agent integration provides some “internal” tools for the agents Organisational information is made accessible in the mental to simplify their interaction with the organisation: state of the agent as beliefs maintenance of a local copy of the organisational state Integration is totally independent of the production of organisational events distribution/communication layer provision of organisational actions 128 / 165 129 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise J -Moise: Jason + Moise– General view Organisational actions in Jason I Example (GroupBoard) ... Jason-CArtAgo Agent joinWorkspace(”ora4mas”,O4MWsp); makeArtifact( Belief Plan ”auction”, Intentions Base Library ”ora4mas.nopl.GroupBoard”, [”auction-os.xml”, auctionGroup, false, true ], GrArtId); J-Moise+ adoptRole(auctioneer); focus(GrArtId); ... Organisational Workspace (CArtAgO) 130 / 165 131 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisational actions in Jason II Organisational perception Example (SchemeBoard) ... makeArtifact( When an agent focus on an Organisational Artifact, the ”sch1”, observable properties (Java objects) are translated to beliefs with ”ora4mas.nopl.SchemeBoard”, the following predicates: [”auction-os.xml”, doAuction, false, true ], specification SchArtId); focus(SchArtId); play(agent, role, group) addScheme(Sch); commitment(agent, mission, scheme) commitMission(mAuctioneer)[artifact˙id(SchArtId)]; goalState(scheme, goal, list of committed agents, list of ... agent that achieved the goal, state of the goal) obligation(agent,norm,goal,dead line) .... 132 / 165 133 / 165
Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisational perception – example Handling organisational events in Jason Whenever something changes in the organisation, the agent architecture updates the agent belief base accordingly producing events (belief update from perception) Example (new agent entered the group) +play(Ag,boss,GId) ¡- .send(Ag,tell,hello). Example (change in goal state) +goalState(Scheme,wsecs,˙,˙,satisfied) : .my˙name(Me) & commitment(Me,mCol,Scheme) ¡- leaveMission(mColaborator,Scheme). Example (signals) +normFailure(N) ¡- .print(”norm failure event: ”, N). 134 / 165 135 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Typical plans for obligations Summary – Moise Example Ensures that the agents follow some of the constraints +obligation(Ag,Norm,committed(Ag,Mission,Scheme),DeadLine) : .my˙name(Ag) specified for the organisation ¡- .print(”I am obliged to commit to ”,Mission); Helps the agents to work together commit˙mission(Mission,Scheme). The organisation is interpreted at runtime, it is not hardwired in the agents code +obligation(Ag,Norm,achieved(Sch,Goal,Ag),DeadLine) : .my˙name(Ag) The agents ‘handle’ the organisation (i.e. their artifacts) ¡- .print(”I am obliged to achieve goal ”,Goal); It is suitable for open systems as no specific agent !Goal[scheme(Sch)]; architecture is required goal˙achieved(Goal,Sch). +obligation(Ag,Norm,What,DeadLine) All available as open source at : .my˙name(Ag) ¡- .print(”I am obliged to ”,What, http://moise.souceforge.net ”, but I don’t know what to do!”). 136 / 165 137 / 165 Introduction EOP OOP Conclusion Putting the Pieces Together INTERNAL BELIEFS GROUPS ROLES EVENTS GOALS SANCTIONS PLANS MISSIONS REWARDS ACTIONS DEONTIC RELATIONS Conclusions PERCEPTIONS NORMS JASON MOISE ? AGENTS ORGANISATIONS Agent Prog. Framework Language CarTaGO JADE Platform Platform SPEECH COMMUNICATION RESOURCES ACTS LANGUAGES LEGACY INTERACTION SERVICES OBJECTS PROCOLS ENVIRONMENTS INTERACTIONS 139 / 165
Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion Exploiting Orthogonality Beyond Orthogonality: Synergetic Integration Treating AOP & EOP & OOP as orthogonal dimensions improving separation of concerns using the best abstraction level and tools to tackle the Exploiting one dimension to e↵ectively design and program specific dimensions, avoiding design pitfalls, such as using agents to implement either non-autonomous entities (e.g., a also aspects related to the other dimensions blackboard agent) or a collection of autonomous entities for instance, using the environment to design, implement and (group agent) represent at runtime the organisation infrastructure promoting openness and heterogeneity Designing and implementing MAS behaviours that are based E.g., heterogeneous agents working in the same organisation, on explicit bindings between the di↵erent dimensions heterogeneous agents working in the same environment, the for instance, exploiting events occurring in the environment to same agent working in di↵erent and heterogeneous represent events that have an e↵ect at the institutional or organisations, the same agent working in di↵erent heterogeneous environments social level Outcome from a programming point of view code more clean and understandable improving modularity, extensibility, reusability 140 / 165 141 / 165 Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion Exploiting Synergy between the A/E Dimensions Exploiting Synergy on A/O Integration Normative deliberative agents Mapping possibility to define mechanisms for agents to evolve within agent actions into environment operations (e.g. CArtAgO) an organisation/several organisations environment observable state/events into agent beliefs possibility to define proper mechanisms for deliberating on the Outcome internalisation/adoption/violation of norms agents with dynamic action repertoire Reorganisation, adaptation of the organisation uniformly implementing any mechanisms (e.g. coordination possibility to define proper mechanisms for mechanism) in terms of actions/percepts diagnosing/evaluating/refining/defining organisations no need to extend agents with special purpose primitives “Deliberative” Organisations exploiting a new type of agent modularity, based on possibility to define dedicated organisational strategies for the externalization [Ricci et al., 2009a] regulation/adaptation of the organisation behaviour (organisational agents) 142 / 165 143 / 165 Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion Exploiting Synergy between the E/O Dimensions E/O Synergy Example: Implementing Regimentation Grounding the organisation infrastructure implemented using environment abstractions Exploiting the environment role of enabler and mediator of ... that agents perceive then as first-class entities of their agent interaction world by providing actions and generating percepts Mapping ! natural place where to embed and enforce organisational state reified by the environment computational organisational rules and norms state a↵ecting action execution behaviour and percepts generation organisational actions/perceptions reified by actions/percepts Examples on the environment state simple: a game-board artifact in an artifact-based organisational functionalities encapsulated by suitably environment designed environment abstractions providing agents actions to make moves Outcome encapsulating and enforcing the rules of the game “the power is back to agents” [H¨bner et al., 2009c] u complex: fully-fledged institutions by perceiving and acting upon that environment, agents can reified into properly programmed environments reason and dynamically adapt the organisation infrastructure 144 / 165 145 / 165
Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion E/O Synergy Example: Implementing Constitutive Rules A proposal: JaCaMo Environment Manual consult External Action Internal Action dimension Exploiting the environment to create, represent, and manage has create dependencies and rules that are meaningful at the Workspace Artifact dispose link Action Agent join dimension organisational level quit A main example: implementing constitutive Work Environment Operation use Agent Plan rules [Searle, 1997] Observable Property update generate Observable Event Trigger event events occurring in concrete environments conveyed as social Belief and institutional events perceive Goal typically represented in the form X counts as Y in C create create delete an example: reaching the environment state S counts as delete Social Scheme achieving the organisational goal G Group Goal commit adopt The integration E/O allows for naturally design and leave leave implementation of these kinds of rules Organisation dimension Role Norm Mission without adding any further concepts wrt the ones belonging to the E/O dimensions composition agent's actions Legend association dimension border dependency concept mapping 146 / 165 147 / 165 Introduction EOP OOP Conclusion Ongoing and Related Research Unifying agents, environments and organisation perspectives Volcano platform [Ricordel and Demazeau, 2002] MASK platform [Occello et al., 2004] MASQ [Stratulat et al., 2009], extending AGRE and AGREEN Embodied organisations [Piunti, 2010] Situated E-Institutions [Campos et al., 2009] Normative programming and infrastructures [H¨bner et al., 2009a, u Tinnemeier et al., 2009, Dastani et al., 2009b] 148 / 165
References [H¨ bner et al., 2009a] H¨ bner, J. F., Boissier, O., and Bordini, R. H. u u (2009a). Normative programming for organisation management infras- [Behrens et al., 2010] Behrens, T., Bordini, R., Braubach, L., Dastani, M., tructures. In MALLOW Workshop on Coordination, Organization, Dix, J., Hindriks, K., Hbner, J., and Pokahr, A. (2010). An interface Institutions and Norms in Agent Systems in Online Communities for agent-environment interaction. In In Proceedings of International (COIN-MALLOW 2009). Workshop on Programming Multi-Agent Systems (ProMAS-8). [H¨ bner et al., 2009b] H¨ bner, J. F., Boissier, O., Kitio, R., and Ricci, A. u u [Bernoux, 1985] Bernoux, P. (1985). La sociologie des organisations. (2009b). Instrumenting multi-agent organisations with organisational Seuil, 3`me edition. e artifacts and agents: “Giving the organisational power back to the agents”. Autonomous Agents and Multi-Agent Systems. DOI-URL: [Bordini et al., 2007] Bordini, R., H¨ bner, J., and Wooldridge, M. (2007). u http://dx.doi.org/10.1007/s10458-009-9084-y. Programming Multi-Agent Systems in AgentSpeak Using Jason. Wiley- Interscience. [H¨ bner et al., 2009c] H¨ bner, J. F., Boissier, O., Kitio, R., and Ricci, A. u u (2009c). Instrumenting Multi-Agent Organisations with Organisational [Bromuri and Stathis, 2008] Bromuri, S. and Stathis, K. (2008). Situating Artifacts and Agents. Journal of Autonomous Agents and Multi-Agent Cognitive Agents in GOLEM. In Weyns, D., Brueckner, S., and De- Systems. mazeau, Y., editors, Engineering Environment-Mediated Multi-Agent [H¨ bner et al., 2002] H¨ bner, J. F., Sichman, J. S., and Boissier, O. u u Systems, volume 5049 of LNCS, pages 115–134. Springer Berlin / Hei- (2002). A model for the structural, functional, and deontic specifica- delberg. tion of organizations in multiagent systems. In Bittencourt, G. and Ra- malho, G. L., editors, Proceedings of the 16th Brazilian Symposium on [Campos et al., 2009] Campos, J., L´pez-S´nchez, M., Rodriguez-Aguilar, o a Artificial Intelligence (SBIA’02), volume 2507 of LNAI, pages 118–128, J. A., and Esteva, M. (2009). Formalising situatedness and adaptation Berlin. Springer. in electronic institutions. In Coordination, Organizations, Institutions and Norms in Agent Systems IV, volume 5428/2009 of LNCS. Springer [H¨ bner et al., 2006] H¨ bner, J. F., Sichman, J. S., and Boissier, O. u u Berlin / Heidelberg. (2006). S-MOISE+: A middleware for developing organised multi-agent systems. In Boissier, O., Dignum, V., Matson, E., and Sichman, J. S., ed- [Carabelea, 2007] Carabelea, C. (2007). Reasoning about autonomy in itors, Coordination, Organizations, Institutions, and Norms in Multi- open multi-agent systems - an approach based on the social power the- Agent Systems, volume 3913 of LNCS, pages 64–78. Springer. ory. in french, ENS Mines Saint-Etienne. [H¨ bner et al., 2007] H¨ bner, J. F., Sichman, J. S., and Boissier, O. u u [Dastani, 2008] Dastani, M. (2008). 2APL: a practical agent programming (2007). Developing Organised Multi-Agent Systems Using the MOISE+ language. Autonomous Agent and Multi-Agent Systems, 16(3):214–248. Model: Programming Issues at the System and Agent Levels. Agent- Oriented Software Engineering, 1(3/4):370–395. [Dastani et al., 2009] Dastani, M., Tinnemeier, N., and Meyer, J.-J. C. (2009). A programming language for normative multi-agent systems. [Malone, 1999] Malone, T. W. (1999). Tools for inventing organizations: In Multi-Agent Systems: Semantics and Dynamics of Organizational Toward a handbook of organizational process. Management Science, Models. IGI-Global. 45(3):425–443. [Dignum and Aldewereld, 2010] Dignum, V. and Aldewereld, H. (2010). [Molesini et al., 2005] Molesini, A., Omicini, A., Denti, E., and Ricci, A. Operetta: Organization-oriented development environment. In Proceed- (2005). SODA: A roadmap to artefacts. In Dikenelli, O., Gleizes, M.- ings of LADS @ MALLOW 2010, pages 14–20. P., and Ricci, A., editors, 6th International Workshop “Engineering Societies in the Agents World” (ESAW’05), pages 239–252, Ku¸adası,s Aydın, Turkey. Ege University. [Esteva et al., 2001] Esteva, M., Rodriguez-Aguiar, J. A., Sierra, C., Gar- cia, P., and Arcos, J. L. (2001). On the formal specification of elec- [Morin, 1977] Morin, E. (1977). La m´thode (1) : la nature de la nature. e tronic institutions. In Dignum, F. and Sierra, C., editors, Proceedings Points Seuil. of the Agent-mediated Electronic Commerce, LNAI 1191, pages 126– 147, Berlin. Springer. [Occello et al., 2004] Occello, M., Baeijs, C., Demazeau, Y., and Koning, J.-L. (2004). MASK: An AEIO toolbox to design and build multi-agent [Esteva et al., 2004] Esteva, M., Rodr´ıguez-Aguilar, J. A., Rosell, B., and systems. In et al., C., editor, Knowledge Engineering and Agent Tech- L., J. (2004). AMELI: An agent-based middleware for electronic insti- nology, IOS Series on Frontiers in AI and Applications. IOS press, Am- tutions. In Jennings, N. R., Sierra, C., Sonenberg, L., and Tambe, M., sterdam. editors, Proc. of the 3rd Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems (AAMAS’04), pages 236–243, New York, USA. [Okuyama et al., 2008] Okuyama, F. Y., Bordini, R. H., and ACM. da Rocha Costa, A. C. (2008). A distributed normative infras- tructure for situated multi-agent organisations. In Baldoni, M., Son, [Ferber and Gutknecht, 1998] Ferber, J. and Gutknecht, O. (1998). A T. C., van Riemsdijk, M. B., and Winiko , M., editors, DALT, volume meta-model for the analysis and design of organizations in multi-agents 5397 of Lecture Notes in Computer Science, pages 29–46. Springer. systems. In Demazeau, Y., editor, Proceedings of the 3rd Interna- tional Conference on Multi-Agent Systems (ICMAS’98), pages 128– [Oliva et al., 2010] Oliva, E., McBurney, P., Omicini, A., and Viroli, M. 135. IEEE Press. (2010). Argumentation and artifacts for negotiation support. Interna- tional Journal of Artificial Intelligence, 4(S10):90–117. Special Issue [Gasser, 2001] Gasser, L. (2001). Organizations in multi-agent systems. on Negotiation and Argumentation in Artificial Intelligence. In Pre-Proceeding of the 10th European Worshop on Modeling Au- tonomous Agents in a Multi-Agent World (MAAMAW’2001), Annecy. [Omicini et al., 2008] Omicini, A., Ricci, A., and Viroli, M. (2008). Ar- tifacts in the A&A meta-model for multi-agent systems. Autonomous [Gˆteau et al., 2005] Gˆteau, B., Boissier, O., Khadraoui, D., and Dubois, a a Agents and Multi-Agent Systems, 17(3):432–456. E. (2005). Moiseinst: An organizational model for specifying rights and duties of autonomous agents. In Third European Workshop on Multi- [Omicini et al., 2004] Omicini, A., Ricci, A., Viroli, M., Castelfranchi, C., Agent Systems (EUMAS 2005), pages 484–485, Brussels Belgium. and Tummolini, L. (2004). Coordination artifacts: Environment-based coordination for intelligent agents. In Proc. of the 3rd Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems (AAMAS’04), vol- [Gutknecht and Ferber, 2000a] Gutknecht, O. and Ferber, J. (2000a). The ume 1, pages 286–293, New York, USA. ACM. MADKIT agent platform architecture. In Agents Workshop on Infras- tructure for Multi-Agent Systems, pages 48–55. [Ossowski, 1999] Ossowski, S. (1999). Co-ordination in Artificial Agent Societies: Social Structures and Its Implications for Autonomous [Gutknecht and Ferber, 2000b] Gutknecht, O. and Ferber, J. (2000b). The Problem-Solving Agents, volume 1535 of LNAI. Springer. MadKit agent platform architecture. In Agents Workshop on Infras- tructure for Multi-Agent Systems, pages 48–55. [Piunti, 2010] Piunti, M. (2010). Embodied organizations: a unifying per- spective in programming agents, organizations and environments. PhD [Hannoun et al., 2000] Hannoun, M., Boissier, O., Sichman, J. S., and thesis. Sayettat, C. (2000). Moise: An organizational model for multi-agent systems. In Monard, M. C. and Sichman, J. S., editors, Proceedings of [Piunti and Ricci, 2009] Piunti, M. and Ricci, A. (2009). Cognitive ar- the International Joint Conference, 7th Ibero-American Conference on tifacts for intelligent agents in mas: Exploiting relevant information AI, 15th Brazilian Symposium on AI (IBERAMIA/SBIA’2000), At- residing in environments. In Knowledge Representation for Agents ibaia, SP, Brazil, November 2000, LNAI 1952, pages 152–161, Berlin. and Multi-Agent Systems (KRAMAS 2008), volume 5605 of LNAI. Springer. Springer. 1
[Piunti et al., 2009a] Piunti, M., Ricci, A., Boissier, O., and Hubner, J. [Ricordel and Demazeau, 2002] Ricordel, P. and Demazeau, Y. (2002). (2009a). Embodying organisations in multi-agent work environments. In VOLCANO: a vowels-oriented multi-agent platform. In Dunin-Keplicz IEEE/WIC/ACM International Conference on Web Intelligence and and Nawarecki, editors, Proceedings of the International Conference Intelligent Agent Technology (WI-IAT 2009), Milan, Italy. of Central Eastern Europe on Multi-Agent Systems (CEEMAS’01), volume 2296 of LNAI, pages 252–262. Springer Verlag. [Piunti et al., 2009b] Piunti, M., Ricci, A., Boissier, O., and H¨ bner, J. F. u (2009b). Embodied organisations in mas environments. In Braubach, [Rocha Costa and Dimuro, 2009] Rocha Costa, A. C. d. and Dimuro, G. L., van der Hoek, W., Petta, P., and Pokahr, A., editors, Proceedings of (2009). A minimal dynamical organization model. In Dignum, V., edi- 7th German conference on Multi-Agent System Technologies (MATES tor, Multi-Agent Systems: Semantics and Dynamics of Organizational 09), Hamburg, Germany, September 9-11, volume 5774 of LNCS, pages Models, chapter XVII, pages 419–445. IGI Global. 115–127. Springer. [Russell and Norvig, 2003] Russell, S. and Norvig, P. (2003). Artificial [Piunti et al., 2008] Piunti, M., Ricci, A., Braubach, L., and Pokahr, Intelligence, A Modern Approach (2nd ed.). Prentice Hall. A. (2008). Goal-directed interactions in artifact-based mas: Jadex agents playing in CARTAGO environments. In Proc. of the 2008 [Santi et al., 2011] Santi, A., Guidi, M., and Ricci, A. (2011). Jaca- IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent android: An agent-based platform for building smart mobile applica- Agent Technology (IAT’08), volume 2. IEEE Computer Society. tions. In Languages, Methodologies and Development Tools for Multi- agent systems, volume 6822 of LNAI. Springer Verlag. [Platon et al., 2007] Platon, E., Mamei, M., Sabouret, N., Honiden, S., [Searle, 1997] Searle, J. R. (1997). The Construction of Social Reality. and Parunak, H. V. (2007). Mechanisms for environments in multi-agent Free Press. systems: Survey and opportunities. Autonomous Agents and Multi- Agent Systems, 14(1):31–47. [Shoham, 1993] Shoham, Y. (1993). Agent-oriented programming. Artif. Intell., 60(1):51–92. [Pynadath and Tambe, 2003] Pynadath, D. V. and Tambe, M. (2003). An automated teamwork infrastructure for heterogeneous software agents [Stratulat et al., 2009] Stratulat, T., Ferber, J., and Tranier, J. (2009). and humans. Autonomous Agents and Multi-Agent Systems, 7(1-2):71– MASQ: towards an integral approach to interaction. In AAMAS (2), 100. pages 813–820. [Ricci et al., 2010a] Ricci, A., Denti, E., and Piunti, M. (2010a). A plat- [Tambe, 1997] Tambe, M. (1997). Towards flexible teamwork. Journal of form for developing SOA/WS applications as open and heterogeneous Artificial Intelligence Reseearch, 7:83–124. multi-agent systems. Multiagent and Grid Systems International Jour- nal (MAGS), Special Issue about “Agents, Web Services and Ontolo- [Tinnemeier et al., 2009] Tinnemeier, N., Dastani, M., Meyer, J.-J., and gies: Integrated Methodologies”. To Appear. van der Torre, L. (2009). Programming normative artifacts with declar- ative obligations and prohibitions. In IEEE/WIC/ACM International [Ricci et al., 2007a] Ricci, A., Omicini, A., Viroli, M., Gardelli, L., and Joint Conference on Web Intelligence and Intelligent Agent Technol- Oliva, E. (2007a). Cognitive stigmergy: Towards a framework based ogy (WI-IAT 2009). on agents and artifacts. In Weyns, D., Parunak, H. V. D., and Michel, F., editors, Environments for MultiAgent Systems III, volume 4389 of [Viroli et al., 2007] Viroli, M., Holvoet, T., Ricci, A., Schelfthout, K., and LNAI, pages 124–140. Springer. Zambonelli, F. (2007). Infrastructures for the environment of multiagent systems. Autonomous Agents and Multi-Agent Systems, 14(1):49–60. [Ricci et al., 2008] Ricci, A., Piunti, M., Acay, L. D., Bordini, R., H¨ bner, u J., and Dastani, M. (2008). Integrating artifact-based environments [Weyns and Holvoet, 2004] Weyns, D. and Holvoet, T. (2004). A formal with heterogeneous agent-programming platforms. In Proceedings of model for situated multi-agent systems. Fundamenta Informaticae, 7th International Conference on Agents and Multi Agents Systems 63(2-3):125–158. (AAMAS08). [Weyns and Holvoet, 2007] Weyns, D. and Holvoet, T. (2007). A reference architecture for situated multiagent systems. In Environments for Mul- [Ricci et al., 2009a] Ricci, A., Piunti, M., and Viroli, M. (2009a). Ex- tiagent Systems III, volume 4389 of LNCS, pages 1–40. Springer Berlin ternalisation and internalization: A new perspective on agent modu- / Heidelberg. larisation in multi-agent system programming. In Dastani, M., Fallah- Seghrouchni, A. E., Leite, J., and Torroni, P., editors, LADS, volume [Weyns et al., 2007] Weyns, D., Omicini, A., and Odell, J. J. (2007). Envi- 6039 of Lecture Notes in Computer Science, pages 35–54. Springer. ronment as a first-class abstraction in multi-agent systems. Autonomous Agents and Multi-Agent Systems, 14(1):5–30. [Ricci et al., 2010b] Ricci, A., Piunti, M., and Viroli, M. (2010b). Environ- ment programming in multi-agent systems – an artifact-based perspec- [Weyns and Parunak, 2007] Weyns, D. and Parunak, H. V. D., editors tive. Autonomous Agents and Multi-Agent Systems. Published Online (2007). Special Issue on Environments for Multi-Agent Systems, vol- with ISSN 1573-7454 (will appear with ISSN 1387-2532). ume 14 (1) of Autonomous Agents and Multi-Agent Systems. Springer Netherlands. [Ricci et al., 2009b] Ricci, A., Piunti, M., Viroli, M., and Omicini, A. (2009b). Environment programming in CArtAgO. In Bordini, R. H., Das- [Weyns et al., 2005] Weyns, D., Parunak, H. V. D., Michel, F., Holvoet, tani, M., Dix, J., and El Fallah-Seghrouchni, A., editors, Multi-Agent T., and Ferber, J. (2005). Environments for multiagent systems: State- Programming: Languages, Platforms and Applications, Vol. 2, pages of-the-art and research challenges. In Weyns, D., Parunak, H. V. D., 259–288. Springer Berlin / Heidelberg. Michel, F., Holvoet, T., and Ferber, J., editors, Environment for Multi- Agent Systems, volume 3374, pages 1–47. Springer Berlin / Heidelberg. [Ricci et al., 2009c] Ricci, A., Piunti, M., Viroli, M., and Omicini, A. (2009c). Environment programming in CArtAgO. In Multi-Agent Pro- [Wooldridge, 2002] Wooldridge, M. (2002). An Introduction to Multi- gramming: Languages,Platforms and Applications,Vol.2. Springer. Agent Systems. John Wiley & Sons, Ltd. [Ricci et al., 2010c] Ricci, A., Santi, A., and Piunti, M. (2010c). Action and perception in multi-agent programming languages: From exogenous to endogenous environments. In In Proceedings of International Work- shop on Programming Multi-Agent Systems (ProMAS-8). [Ricci et al., 2007b] Ricci, A., Viroli, M., and Omicini, A. (2007b). The A&A programming model & technology for developing agent environ- ments in MAS. In Dastani, M., El Fallah Seghrouchni, A., Ricci, A., and Winiko , M., editors, Programming Multi-Agent Systems, volume 4908 of LNAI, pages 91–109. Springer Berlin / Heidelberg. [Ricci et al., 2007c] Ricci, A., Viroli, M., and Omicini, A. (2007c). CArtAgO: A framework for prototyping artifact-based environments in MAS. In Weyns, D., Parunak, H. V. D., and Michel, F., editors, En- vironments for MultiAgent Systems III, volume 4389 of LNAI, pages 67–86. Springer. 3rd International Workshop (E4MAS 2006), Hakodate, Japan, 8 May 2006. Selected Revised and Invited Papers. 2

More Related Content

PDF
T3 Agent oriented programming languages
byEASSS 2012
 
PDF
T12 Distributed search and constraint handling
byEASSS 2012
 
PDF
T14 Argumentation for agent societies
byEASSS 2012
 
PDF
T9. Trust and reputation in multi-agent systems
byEASSS 2012
 
PDF
T13 Agent coordination in planning and scheduling
byEASSS 2012
 
PDF
T0. Multiagent Systems and Electronic Institutions
byEASSS 2012
 
PDF
Summary of a Recommender Systems Survey paper
byChangsung Moon
 
PDF
T4 Introduction to the modelling and verification of, and reasoning about mul...
byEASSS 2012
 
T3 Agent oriented programming languages
byEASSS 2012
 
T12 Distributed search and constraint handling
byEASSS 2012
 
T14 Argumentation for agent societies
byEASSS 2012
 
T9. Trust and reputation in multi-agent systems
byEASSS 2012
 
T13 Agent coordination in planning and scheduling
byEASSS 2012
 
T0. Multiagent Systems and Electronic Institutions
byEASSS 2012
 
Summary of a Recommender Systems Survey paper
byChangsung Moon
 
T4 Introduction to the modelling and verification of, and reasoning about mul...
byEASSS 2012
 

Similar to T2. Organization and Environment oriented programming

PDF
Multi Level Education (181)
byRebecca Obounou
 
PDF
Pbmr Ea Conceptual Framework Poster V01.04
bySean Manyaapelo
 
PDF
Landscape Architecture Portfolio
bycmmcdowell
 
PDF
Day in the Life of a Computer Scientist
byJustin Brunelle
 
PPT
Architecture solution architecture method
byChris Eaton
 
PPTX
Systems engineering for project managers - what you need to know
byAssociation for Project Management
 
PDF
Software archiecture lecture09
byLuktalja
 
PPT
SXSWedu 2012 Talk: OER that is Easy to Adapt, Edit, and Translate
byoerpub
 
KEY
Supporting Developers, Supporting Research
byPaul Walk
 
KEY
Demo: ALIVE@AAMAS'2010
byKnowledge Engineering and Machine Learning Group
 
PPTX
Thinking Aloud: University Enterprise Architecture Themes and Horizons
byAlison Pope
 
DOC
[doc].doc
bybutest
 
PDF
Distributed Collaborative Enterprise Modeling Tutorial @ CAiSE'07
byGraham McLeod
 
PDF
IT – a 21st Century Profession Ian Ryder
bySFIA User Forum
 
PDF
Methodology
byGeoff Leigh
 
PDF
Open- Closed Systems in innovation projects@ UK Systems Society Conference 2009
byMaria Kapsali
 
PDF
Uml (grasp)
byDjordje Badza
 
KEY
The Changing Role of the Developer in HE
byPaul Walk
 
PDF
T Bahr M Lindlar Goportis Digital Preservation Pilot
byFuture Perfect 2012
 
PDF
Globalizing Existing Applications
byInfosys
 
Multi Level Education (181)
byRebecca Obounou
 
Pbmr Ea Conceptual Framework Poster V01.04
bySean Manyaapelo
 
Landscape Architecture Portfolio
bycmmcdowell
 
Day in the Life of a Computer Scientist
byJustin Brunelle
 
Architecture solution architecture method
byChris Eaton
 
Systems engineering for project managers - what you need to know
byAssociation for Project Management
 
Software archiecture lecture09
byLuktalja
 
SXSWedu 2012 Talk: OER that is Easy to Adapt, Edit, and Translate
byoerpub
 
Supporting Developers, Supporting Research
byPaul Walk
 
Demo: ALIVE@AAMAS'2010
byKnowledge Engineering and Machine Learning Group
 
Thinking Aloud: University Enterprise Architecture Themes and Horizons
byAlison Pope
 
[doc].doc
bybutest
 
Distributed Collaborative Enterprise Modeling Tutorial @ CAiSE'07
byGraham McLeod
 
IT – a 21st Century Profession Ian Ryder
bySFIA User Forum
 
Methodology
byGeoff Leigh
 
Open- Closed Systems in innovation projects@ UK Systems Society Conference 2009
byMaria Kapsali
 
Uml (grasp)
byDjordje Badza
 
The Changing Role of the Developer in HE
byPaul Walk
 
T Bahr M Lindlar Goportis Digital Preservation Pilot
byFuture Perfect 2012
 
Globalizing Existing Applications
byInfosys
 

Recently uploaded

PDF
Orchard Floor Managment.pdf Orchard Floor Management
bybisensharad
 
PDF
Prescription Writing- Elements, Parts, and Exercises
byDr. Ishita Agarwal
 
PDF
Ketogenic diet in Epilepsy in children..
bymirzasania0810
 
PDF
BỘ TEST KIỂM TRA CUỐI HỌC KÌ 1 - TIẾNG ANH 6-7-8-9 GLOBAL SUCCESS - PHIÊN BẢN...
byNguyen Thanh Tu Collection
 
PPTX
Rectal Surgery in Senior Citiizens .pptx
byJohn Thanakumar
 
PDF
Principles and Practices of GST 2.0 Study material
bySri Ramakrishna College of Arts and science
 
PDF
Chaplin's Visual Critique of Modernism: Modern Times and The Great Dictator
bykrutivyas2005
 
PDF
Handwritten notes of Toxicity 1. Genotoxicity 2 Carcinogenicity 3 Teratogenic...
byjagdeepsinghynr7
 
PPTX
Searching in PubMed andCochrane_Practical Presentation.pptx
bySystematic Reviews Network (SRN)
 
PPTX
How to Automate Quality Checks in Odoo 18 Quality App
byCeline George
 
PDF
Projecte de la porta de primer B: L'antic Egipte
byeduardrubio1
 
PDF
Projecte de la porta d'i5B: Els animals marins
byeduardrubio1
 
PPTX
What is the Post load hook in Odoo 18
byCeline George
 
PDF
The Pity of War: Form, Fragment, and the Artificial Echo | Understanding War ...
byNidhi Pandya
 
PDF
The Tale of Melon City poem ppt by Sahasra
bybitrasahasra
 
PDF
The Tale of Melon City poem ppt by Sahasra
bybitrasahasra
 
PDF
All Students Workshop 25 Yoga Wellness by LDMMIA
by©LDMMIA, ©Reiki Yoga
 
PPTX
ICH Harmonization A Global Pathway to Unified Drug Regulation.pptx
byDr. Smita Kumbhar
 
PDF
Unit-III pdf (Basic listening Skill, Effective Writing Communication & Writin...
bySayyadTarannum
 
PPTX
Unit I — Introduction to Anatomical Terms and Organization of the Human Body
byRAKESH SAJJAN
 
Orchard Floor Managment.pdf Orchard Floor Management
bybisensharad
 
Prescription Writing- Elements, Parts, and Exercises
byDr. Ishita Agarwal
 
Ketogenic diet in Epilepsy in children..
bymirzasania0810
 
BỘ TEST KIỂM TRA CUỐI HỌC KÌ 1 - TIẾNG ANH 6-7-8-9 GLOBAL SUCCESS - PHIÊN BẢN...
byNguyen Thanh Tu Collection
 
Rectal Surgery in Senior Citiizens .pptx
byJohn Thanakumar
 
Principles and Practices of GST 2.0 Study material
bySri Ramakrishna College of Arts and science
 
Chaplin's Visual Critique of Modernism: Modern Times and The Great Dictator
bykrutivyas2005
 
Handwritten notes of Toxicity 1. Genotoxicity 2 Carcinogenicity 3 Teratogenic...
byjagdeepsinghynr7
 
Searching in PubMed andCochrane_Practical Presentation.pptx
bySystematic Reviews Network (SRN)
 
How to Automate Quality Checks in Odoo 18 Quality App
byCeline George
 
Projecte de la porta de primer B: L'antic Egipte
byeduardrubio1
 
Projecte de la porta d'i5B: Els animals marins
byeduardrubio1
 
What is the Post load hook in Odoo 18
byCeline George
 
The Pity of War: Form, Fragment, and the Artificial Echo | Understanding War ...
byNidhi Pandya
 
The Tale of Melon City poem ppt by Sahasra
bybitrasahasra
 
The Tale of Melon City poem ppt by Sahasra
bybitrasahasra
 
All Students Workshop 25 Yoga Wellness by LDMMIA
by©LDMMIA, ©Reiki Yoga
 
ICH Harmonization A Global Pathway to Unified Drug Regulation.pptx
byDr. Smita Kumbhar
 
Unit-III pdf (Basic listening Skill, Effective Writing Communication & Writin...
bySayyadTarannum
 
Unit I — Introduction to Anatomical Terms and Organization of the Human Body
byRAKESH SAJJAN
 

T2. Organization and Environment oriented programming

  • 1.
    Organisation and Environment Oriented Programming Outline of the course Introduction Olivier Boissier1 , Rafael H. Bordini2 , EOP Jomi F. H¨bner3 , Alessandro Ricci4 u About Environment Oriented Programming 1 EMSE, 2 PUC-RS, A&A and CArtAgO France Brazil Olivier.Boissier@emse.fr R.Bordini@pucrs.br OOP 3 DAS-UFSC, Brazil 4 University of Bologna, Italy About Organisation Oriented Programming jomi@das.ufsc.br a.ricci@unibo.it Moise Conclusions European Agent Systems Summer School Practical Exercise: a hands-on lab session! May, 2012 — Valencia, Spain Introduction EOP OOP Conclusion Abstractions in Multi-Agent Systems schema mission role ORGAMISATION org LEVEL Introduction agent AGENT LEVEL artifact ENDOGENOUS ENVIRONMENT wsp LEVEL EXOGENOUS ENVIRONMENT 4 / 165 network node Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion Abstractions in Multi-Agent Systems Agent Oriented Programming Individual Agent Level: autonomy, situatedness Cognitive Concepts: beliefs, desires, goals, intentions, plans Proposed by Shoham [Shoham, 1993] Reasoning Cycle: sense/reason/act, reactive/pro-active Use of mentalistic notions and a societal view of behaviour computation (anthropomorphism) Environment Level: resources and services that agents can Levels of abstraction: Agents – Organisations – Environment access and control; sensing and acting in an environment Programming languages for agents have developed a lot Social and Organisation Level: cooperation, coordination, since then, but still not a mature paradigm regulation patterns Programming languages/platforms for organisation and Roles: rights, responsibilities, ... environment are also being developed Organisational Rules: constraints on roles and their interactions, norms, deadlines, ... Some agent development platforms have a formal basis Organisational Structures: topology of interaction patterns Many influenced by the BDI agent architecture and relations over activity control 5 / 165 6 / 165
  • 2.
    Introduction EOP OOPConclusion Introduction EOP OOP Conclusion BDI Architecture Programming Languages for Cognitive Agents Programming Languages for Multi-Agent Systems E.g., Jason, Jadex, JACK, 2APL, GOAL, Brahms, JIAC, Agent Intentional Stance (Dennett) Factory, MetateM, Golog variants, ... Practical Reasoning (Bratman) IRMA (Bratman, Isreal, Pollack) Architecture to represent an agent mental state: PRS (George↵, Lansky) Beliefs: information available to agent (e.g., about the dMARS (Kinny) environment or other agents) Goals: states of a↵airs that the agent wants to achieve BDI Logics and Agent Architecture (Rao, George↵) Events: changes in agents beliefs or goals Wooldridge, Singh, ... Capabilities: reusable modules of activities that the agent can perform Plans: reasoning about courses of action to achieve goals Rules: reasoning about beliefs 7 / 165 8 / 165 Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion Programming Languages for Cognitive Agents Programming Languages/Platforms for Organisations Concepts used to specify the state of an organisation: Some steps of a Reasoning Cycle: Agents, Roles, Groups Determining Relevant Plans for Handling Events Norms, Obligations, Prohibitions, Permissions, Violations Select a Plan for Execution Dependency, Power, Delegation, Information flow relations Execute Part of an Intended Plans Deadlines, Sanctions, Rewards Handle Plan Failures Management Infrastructure to control and coordinate agent behaviour at run-time: Agent Interpreter is an infinite loop of such reasoning cycles. Endogenous: The control is a part of the agent program The architecture and reasoning cycle together with the agent Exogenous: The control is performed by an external system program (specially plans) determine the behaviour of the agent. Monitoring Agent Behaviour Enforcing Organisational Rules Regimenting Organisational Rules 9 / 165 10 / 165 Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion Programming Languages/Platforms for Environments Hands-on Part of this Course We will use JaCaMo Artifacts to represent the state of the environment First fully operational, unified platform covering the 3 main Access to Databases/Services/etc., Coordination, Interaction levels of abstractions for multi-agent oriented programming Environment “objects”, i.e., non-proactive entities JaCaMo = Jason + CArtAgO + Moise Processing Operations on Artifacts http://jacamo.sourceforge.net Realising the e↵ects of environments actions http://jason.sourceforge.net Providing events related to sensing the environment http://cartago.sourceforge.net Synchronising agent actions http://moise.sourceforge.net At the right level of abstraction for a multi-agent system More than the sum of 3 successful platforms Revealing the full potential of Multi-Agent Oriented Programming 11 / 165 12 / 165
  • 3.
    Outline Environment Oriented 2 Environment Programming Why Environment Programming in MAS Programming Basic Level Advanced Level — EOP — A&A and CArtAgO Conclusions and Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Back to the Notion of Environment in MAS Single Agent Perspective sensors The notion of environment is intrinsically related to the db ack pe fee PERCEPTION rce notion of agent and multi-agent system pts ENVIRONMENT “An agent is a computer system that is situated in some DECISION acti do environment and that is capable of autonomous action in ons tion to ac ACTION this environment in order to meet its design objective” [Wooldridge, 2002] effectors / actuators “An agent is anything that can be viewed as perceiving its environment through sensors and acting upon the environment through e↵ectors. Perception ” [Russell and Norvig, 2003] process inside agent inside of attaining awareness or Including both physical and software environments understanding sensory information, creating percepts perceived form of external stimuli or their absence Actions the means to a↵ect, change or inspect the environment 15 / 165 16 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Multi-Agent Perspective Why Environment Programming Basic level to create testbeds for real/external environments to ease the interface/interaction with existing software environments Advanced level to uniformly encapsulate and modularise functionalities of the MAS out of the agents typically related to interaction, coordination, organisation, security externalisation this implies changing the perspective on the environment environment as a first-class abstraction of the MAS In evidence endogenous environments (vs. exogenous ones) programmable environments overlapping spheres of visibility and influence ..which means: interaction 17 / 165 18 / 165
  • 4.
    Introduction EOP OOPConclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Environment Programming: General Issues Basic Level Overview MAS MAS ENVIRONMENT actions Defining the interface SIMULATED REAL WORLD mimicking actions, perceptions WORLD (PHYSICAL OR COMPUTATIONAL) data-model OR Defining the environment computational model & architecture how the environment works INTERFACE EXTERNAL WORLD structure, behaviour, topology (PHYSICAL OR core aspects to face: concurrency, distribution COMPUTATIONAL) OR Defining the environment programming model how to program the environment WRAPPER TO EXISTING Example: percepts JAVA TECHNOLOGY AGENTS PLATFORM 19 / 165 20 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Basic Level: Features An Example: Jason [Bordini et al., 2007] Flexible Java-based Environment API Environment conceptually conceived as a single monolitic Environment base class to be specialised block executeAction method to specify action semantics addPercept to generate percepts providing actions, generating percepts Environment API Environment User Agent -globalPercepts: List<Literal> Environment Architecture to define the set of actions and program actions -agPercepts: Map<String,List<Literal>> +init(String[] args) computational behaviour +stop() getPercepts which include the generation of percepts +getPercepts(String agName): List<Literal> +executeAction(String agName, Structure action): boolean typically implemented using as single object/class in OO such +addPercept(String agName, Literal p) as Java +removePercept(String agName, Literal p) ... method to execute actions executeAction fields to store the environment state UserEnvironment available in many agent programming languages/frameworks +init(String[] args) change +executeAction(String agName, Structure action): boolean e.g., Jason, 2APL, GOAL, JADEX percepts 21 / 165 22 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up MARS Environment in Jason Jason Agents Playing on Mars // mars robot 1 public class MarsEnv extends Environment { ... private MarsModel model; ... private MarsView view; /* creates the agents perception /* Initial beliefs */ * based on the MarsModel */ +!take(S,L) : true public void init(String[] args) { void updatePercepts() { at(P) :- pos(P,X,Y) & pos(r1,X,Y). <- !ensure_pick(S); model = new MarsModel(); !at(L); view = new MarsView(model); clearPercepts(); /* Initial goal */ drop(S). model.setView(view); updatePercepts(); Location r1Loc = model.getAgPos(0); !check(slots). +!ensure_pick(S) : garbage(r1) } Location r2Loc = model.getAgPos(1); <- pick(garb); /* Plans */ !ensure_pick(S). public boolean executeAction(String ag, Structure action) { Literal pos1 = Literal.parseLiteral +!ensure_pick(_). String func = action.getFunctor(); ("pos(r1," + r1Loc.x + "," + r1Loc.y + ")"); +!check(slots) : not garbage(r1) if (func.equals("next")) { Literal pos2 = Literal.parseLiteral <- next(slot); +!at(L) : at(L). model.nextSlot(); ("pos(r2," + r2Loc.x + "," + r2Loc.y + ")"); !!check(slots). +!at(L) <- ?pos(L,X,Y); } else if (func.equals("move_towards")) { int x = (int)((NumberTerm)action.getTerm(0)).solve(); addPercept(pos1); +!check(slots). move_towards(X,Y); int y = (int)((NumberTerm)action.getTerm(1)).solve(); addPercept(pos2); !at(L). model.moveTowards(x,y); +garbage(r1) : not .desire(carry_to(r2)) } else if (func.equals("pick")) { if (model.hasGarbage(r1Loc)) { <- !carry_to(r2). model.pickGarb(); addPercept(Literal.parseLiteral("garbage(r1)")); } else if (func.equals("drop")) { } +!carry_to(R) model.dropGarb(); <- // remember where to go back } else if (func.equals("burn")) { if (model.hasGarbage(r2Loc)) { ?pos(r1,X,Y); model.burnGarb(); addPercept(Literal.parseLiteral("garbage(r2)")); -+pos(last,X,Y); } else { } return false; } // carry garbage to r2 } !take(garb,R); class MarsModel extends GridWorldModel { ... } updatePercepts(); // goes back and continue to check return true; class MarsView extends GridWorldView { ... } !at(last); } } ... !!check(slots). ... 23 / 165 24 / 165
  • 5.
    Introduction EOP OOPConclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Another Example: 2APL [Dastani, 2008] Example: Block-world Environment in 2APL package blockworld; 2APL public class Env extends apapl.Environment { BDI-based agent-oriented programming language integrating declarative programming constructs (beliefs, goals) and public void enter(String agent, Term x, Term y, Term c){...} imperative style programming constructs (events, plans) Java-based Environment API public Term sensePosition(String agent){...} Environment base class public Term pickup(String agent){...} implementing actions as methods public void north(String agent){...} inside action methods external events can be generated to be perceived by agents as percepts ... } 25 / 165 26 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up 2APL Agents in the block-world Environment Interface Stardard – EIS Initiative Recent initiative supported by main APL research BeliefUpdates: ... { bomb(X,Y) } RemoveBomb(X,Y){ not bomb(X,Y) } groups [Behrens et al., 2010] { true } AddBomb(X,Y) { bomb(X,Y) } PC-rules: { carry(bomb) } { not carry(bomb) } Drop( ) PickUp( ) { { not carry(bomb)} carry(bomb) } goto( X, Y ) <- true | { GOAL, 2APL, GOAL, JADEX, JASON @blockworld( sensePosition(), POS ); Beliefs: B(POS = [A,B]); Goal of the initiative start(0,1). if B(A > X) then bomb(3,3). clean( blockWorld ) :- { @blockworld( west(), L ); goto( X, Y ) design and develop a generic environment interface standard not bomb(X,Y) , not carry(bomb). } else if B(A < X) then a standard to connect agents to environments Plans: B(start(X,Y)) ; { @blockworld( east(), L ); goto( X, Y ) ... environments such as agent testbeds, commercial @blockworld( enter( X, Y, blue ), L ) } applications, video games.. else if B(B > Y) then Goals: { @blockworld( north(), L ); clean( blockWorld ) goto( X, Y ) Principles } PG-rules: clean( blockWorld ) <- bomb( X, Y ) | else if B(B < Y) then { @blockworld( south(), L ); wrapping already existing environments { goto( X, Y ); } goto( X, Y ) creating new environments by connecting already existing @blockworld( pickup( ), L1 ); PickUp( ); } apps RemoveBomb( X, Y ); goto( 0, 0 ); ... creating new environments from scratch @blockworld( drop( ), L2 ); } Drop( ) Requirements ... generic reuse 27 / 165 28 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up EIS Meta-Model Environment Interface Features Interface functions attaching, detaching, and notifying observers (software design pattern); registering and unregistering agents; adding and removing entities; managing the agents-entities-relation; performing actions and retrieving percepts; managing the environment By means of the Env. Interface agents perform actions and Interface Intermediate language collect percepts to facilitate data-exchange actually actions/percepts are issued to controllable entities in encoding percepts, actions, events environment model represent the agent bodies, with e↵ectors and sensors 29 / 165 30 / 165
  • 6.
    Introduction EOP OOPConclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Advanced Level Overview Three Support Levels [Weyns et al., 2007] Vision: environment as a first-class abstraction in MAS [Weyns et al., 2007, Ricci et al., 2010b] application or endogenous environments, i.e. that environment which is an explicit part of the MAS Basic interface support providing an exploitable design & programming abstraction Abstraction support level to build MAS applications Interaction-mediation support level Outcome distinguishing clearly between the responsibilities of agent and environment separation of concerns improving the engineering practice 31 / 165 32 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Basic Interface Support Abstraction Support Bridges the conceptual gap between the agent abstraction The environment enables agents to access the deployment and low-level details of the deployment context context shields low-level details of the deployment context i.e. the hardware and software and external resources with which the MAS interacts 33 / 165 34 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Interaction-Mediation Support Environment Definition Revised Regulate the access to shared resources Mediate interaction between agents Environment definition revised [Weyns et al., 2007] The environment is a first-class abstraction that provides the surrounding conditions for agents to exist and that mediates both the interaction among agents and the access to resources 35 / 165 36 / 165
  • 7.
    Introduction EOP OOPConclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Research on Environments for MAS Environment Programming Environment as first-class programming Environments for Multi-Agent Systems research field / abstraction [Ricci et al., 2010b] E4MAS workshop series [Weyns et al., 2005] software designers and engineers perspective di↵erent themes and issues (see JAAMAS Special endogenous environments (vs. exogenous one) Issue [Weyns and Parunak, 2007] for a good survey) programming MAS = mechanisms, architectures, infrastructures, programming Agents + programming Environment applications [Platon et al., 2007, Weyns and Holvoet, 2007, Weyns and Holvoet, 2004, Viroli et al., 2007] ..but this will be extended to include OOP in next part the main perspective is (agent-oriented) software engineering Environment as first-class runtime abstraction for agents Focus of this tutorial: the role of the environment agent perspective abstraction in MAS programming to be observed, used, adapted, constructed, ... environment programming Defining computational and programming frameworks/models also for the environment part 37 / 165 38 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Computational Frameworks for Environment Programming Models for the Environment: Desiderata Programming: Issues Abstraction keeping the agent abstraction level e.g. no agents sharing and Defining the environment interface calling OO objects actions, percepts, data model e↵ective programming models for controllable and observable contract concept, as defined in software engineering contexts computational entities (Design by Contract) Modularity Defining the environment computational model away from the monolithic and centralised view environment structure, behaviour Orthogonality Defining the environment distribution model wrt agent models, architectures, platforms topology support for heterogeneous systems 39 / 165 40 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Programming Models for the Environment: Desiderata Existing Computational Frameworks AGRE / AGREEN / MASQ [Stratulat et al., 2009] AGRE – integrating the AGR (Agent-Group-Role) organisation model with a notion of environment Environment used to represent both the physical and social part of interaction Dynamic extensibility AGREEN / MASQ – extending AGRE towards a unified dynamic construction, replacement, extension of environment representation for physical, social and institutional parts environments support for open systems Based on MadKit platform [Gutknecht and Ferber, 2000a] Reusability GOLEM [Bromuri and Stathis, 2008] reuse of environment parts for di↵erent kinds of applications Logic-based framework to represent environments for situated cognitive agents composite structure containing the interaction between cognitive agents and objects A&A and CArtAgO [Ricci et al., 2010b] introducing a computational notion of artifact to design and implement agent environments 41 / 165 42 / 165
  • 8.
    Introduction EOP OOPConclusion Introduction Basic Advanced CArtAgO Wrap-up Agents and Artifacts (A&A) Conceptual Model: Background Human Metaphor CLOCK WHITEBOARD BAKERY artifact artifact workspace agents can join dynamically the workspace A&A and CArtAgO ARCHIVE artifact COM. CHANNEL artifact RESOURCE artifact TASK SCHEDULER artifact 44 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up A&A Basic Concepts [Omicini et al., 2008] A&A Programming Model Features [Ricci et al., 2007b] Agents Abstraction autonomous, goal-oriented pro-active entities create and co-use artifacts for supporting their activities artifacts as first-class resources and tools for agents besides direct communication Modularisation Artifacts artifacts as modules encapsulating functionalities, organized non-autonomous, function-oriented, stateful entities in workspaces controllable and observable Extensibility and openness modelling the tools and resources used by agents artifacts can be created and destroyed at runtime by agents designed by MAS programmers Reusability Workspaces artifacts (types) as reusable entities, for setting up di↵erent grouping agents & artifacts kinds of environments defining the topology of the computational environment 45 / 165 46 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up A&A Meta-Model in More Detail [Ricci et al., 2010b] Artifact Abstract Representation Manual consult SIGNALS has ObsPropName(Args) create OBSERVABLE Workspace Artifact dispose ... PROPERTIES link USAGE link INTERFACE OperationX(Params) Operation use Agent Environment ... generate OPERATIONS update Observable perceive Event OperationY(Params) Observable perceive ... Property observe LINK join INTERFACE quit 47 / 165 48 / 165
  • 9.
    Introduction EOP OOPConclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up A World of Artifacts A Simple Taxonomy Individual or personal artifacts designed to provide functionalities for a single agent use state available n_records 1001 e.g. an agenda for managing deadlines, a library... count 5 table_names ... wsdl ... inc state true ... ... Social artifacts reset switch GetLastTradePrice createTable ... addRecord query designed to provide functionalities for structuring and a flag a counter a Stock Quote Web Service ... managing the interaction in a MAS a data-base coordination artifacts [Omicini et al., 2004], organisation artifacts, ... n_items 0 next_todo check_plant max_items 100 last_todo ... clearEvents out e.g. a blackboard, a game-board,... postEvent in put setTodo get cancelTodo registerForEvs rd Boundary artifacts a bounded buffer an agenda an event service a tuple space to represent external resources/services e.g. a printer, a Web Service to represent devices enabling I/O with users e.g GUI, console, etc. 49 / 165 50 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Actions and Percepts in Artifact-Based Environments Interaction Model: Use Explicit semantics defined by the (endogenous) environment [Ricci et al., 2010c] success/failure semantics, execution semantics defining the contract (in the SE acceptation) provided by the PropName Value PropName Value environment ... op(parms) ... action actions ! artifacts’ operation op(Params) AGENT the action repertoire is given by the dynamic set of operations provided by the overall set of artifacts available in the workspace can be changed by creating/disposing artifacts action success/failure semantics is defined by operation semantics Performing an action corresponds to triggering the execution of an operation percepts ! artifacts’ observable properties + signals acting on artifact’s usage interface properties represent percepts about the state of the environment signals represent percepts concerning events signalled by the environment 51 / 165 52 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Interaction Model: Operation execution Interaction Model: Observation SIGNALS PropName Value OBS PROPERTIES CHANGE Value ... op(parms) ... PropName(Value). action PropName(Value). op(Params) ... PropName Value AGENT OPERATION EXECUTION PropName Value Belief base ... action completion (or alike) focus - with success or failure - AGENT OBSERVER a process structured in one or multiple transactional steps asynchronous with respect to agent ...which can proceed possibly reacting to percepts and Agents can dynamically select which artifacts to observe executing actions of other plans/activities predefined focus/stopFocus actions operation completion causes action completion action completion events with success or failure, possibly with action feedbacks 53 / 165 54 / 165
  • 10.
    Introduction EOP OOPConclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Interaction Model: Observation Artifact Linkability PropName(Value). PropName(Value). ... PropName Value PropName Value Belief base ... (or alike) use linkedOp AGENT OBSERVER WSP-X WSP-Y By focussing an artifact Basic mechanism to enable inter-artifact interaction observable properties are mapped into agent dynamic linking artifacts through interfaces (link interfaces) knowledge about the state of the world, as percepts operations triggered by an artifact over an other artifact e.g. belief base Useful to design & program distributed environments signals are mapped as percepts related to observable events realised by set of artifacts linked together possibly hosted in di↵erent workspaces 55 / 165 56 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Artifact Manual CArtAgO Common ARtifact infrastructure for AGent Open Agent-readable description of artifact’s... environment (CArtAgO) [Ricci et al., 2009b] ...functionality what functions/services artifacts of that type provide Computational framework / infrastructure to implement and ...operating instructions run artifact-based environment [Ricci et al., 2007c] how to use artifacts of that type Java-based programming model for defining artifacts set of basic API for agent platforms to work within Towards advanced use of artifacts by intelligent artifact-based environment agents [Piunti et al., 2008] dynamically choosing which artifacts to use to accomplish Distributed and open MAS their tasks and how to use them workspaces distributed on Internet nodes strong link with Semantic Web research issues agents can join and work in multiple workspace at a time Work in progress Role-Based Access Control (RBAC) security model defining ontologies and languages for describing the manuals Open-source technology available at http://cartago.sourceforge.net 57 / 165 58 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Integration with Agent Languages and Platforms JaCa Platform Integration of CArtAgO with Jason language/platform a JaCa program is a dynamic set of Jason agents working Integration with existing agent platforms [Ricci et al., 2008] together in one or multiple CArtAgO workspaces by means of bridges creating an action/perception interface and doing data binding Mapping actions Outcome Jason agent external actions are mapped onto artifacts’ developing open and heterogenous MAS operations introducing a further perspective on interoperability besides percepts the ACL’s one artifacts’ observable properties are mapped onto agent beliefs sharing and working in a common work environment artifacts’ signals are mapped as percepts related to common object-oriented data-model observable events data-model Jason data-model is extended to manage also (Java) objects 59 / 165 60 / 165
  • 11.
    Introduction EOP OOPConclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Example 1: A Simple Counter Artifact Example 1: User and Observer Agents class Counter extends Artifact { USER(S) OBSERVER(S) void init(){ defineObsProp("count",0); count 5 !create_and_use. !observe. } +!create_and_use : true +!observe : true @OPERATION void inc(){ <- !setupTool(Id); <- ?myTool(C); // discover the tool // use focus(C). ObsProperty p = getObsProperty("count"); inc inc; p.updateValue(p.intValue() + 1); // second use specifying the Id +count(V) signal("tick"); inc [artifact_id(Id)]. <- println(“observed new value: “,V). } } // create the tool +tick [artifact_name(Id,”c0”)] +!setupTool(C): true <- println(“perceived a tick”). <- makeArtifact("c0","Counter",C). +?myTool(CounterId): true <- lookupArtifact(“c0”,CounterId). Some API spots -?myTool(CounterId): true <- .wait(10); Artifact base class ?myTool(CounterId). @OPERATION annotation to mark artifact?s operations set of primitives to work define/update/.. observable properties signal primitive to generate signals Working with the shared counter 61 / 165 62 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Pre-defined Artifacts Example 2: Coordination Artifacts – A Bounded Bu↵er Each workspace contains by default a predefined set of public class BoundedBuffer extends Artifact { private LinkedList<Item> items; private int nmax; artifacts void init(int nmax){ items = new LinkedList<Item>(); providing core and auxiliary functionalities defineObsProperty("n_items",0); this.nmax = nmax; i.e. a pre-defined repertoire of actions available to agents... } n_items 5 @OPERATION void put(Item obj){ Among the others await("bufferNotFull"); items.add(obj); getObsProperty("n_items").updateValue(items.size()); put workspace, type: cartago.WorkspaceArtifact } get functionalities to manage the workspace, including security @OPERATION void get(OpFeedbackParam<Item> res) { await("itemAvailable"); Item item = items.removeFirst(); operations: makeArtifact, lookupArtifact, focus,... res.set(item); getObsProperty("n_items").updateValue(items.size()); node, type: cartago.NodeArtifact } @GUARD boolean itemAvailable(){ return items.size() > 0; } core functionalities related to a node @GUARD boolean bufferNotFull(Item obj){ return items.size() < nmax; } operations: createWorkspace, joinWorkspace, ... } console, type cartago.tools.Console operations: println,... Basic operation features blackboard, type cartago.tools.TupleSpace output parameters to represent action feedbacks operations: out, in, rd, ... long-term operations, with a high-level support for .... synchronization (await primitive, guards) 63 / 165 64 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Example 2: Producers and Consumers Remarks PRODUCERS CONSUMERS item_to_produce(0). !consume. !produce. +!consume: true +!produce: true <- ?bufferReady; <- !setupTools(Buffer); !consumeItems. Process-based operation execution semantics !produceItems. +!consumeItems: true action/operation execution can be long-term +!produceItems : true <- get(Item); <- ?nextItemToProduce(Item); !consumeItem(Item); action/operation execution can overlap put(Item); !!consumeItems. !!produceItems. key feature for implementing coordination functionalities +!consumeItem(Item) : true +?nextItemToProduce(N) : true <- .my_name(Me); Operation with output parameters as action feedbacks <- -item_to_produce(N); println(Me,": ",Item). +item_to_produce(N+1). +?bufferReady : true +!setupTools(Buffer) : true <- lookupArtifact("myBuffer",_). <- makeArtifact("myBuffer","BoundedBuffer", -?bufferReady : true [10],Buffer). <-.wait(50); ?bufferReady. -!setupTools(Buffer) : true <- lookupArtifact("myBuffer",Buffer). 65 / 165 66 / 165
  • 12.
    Introduction EOP OOPConclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Action Execution & Blocking Behaviour Example 3: Internal Processes – A Clock CLOCK CLOCK USER AGENT public class Clock extends Artifact { !test_clock. Given the action/operation map, by executing an action the boolean working; final static long TICK_TIME = 100; +!test_clock <- makeArtifac("myClock","Clock",[],Id); focus(Id); intention/activity is suspended until the corresponding void init(){ working = false; } +n_ticks(0); start; operation has completed or failed @OPERATION void start(){ if (!working){ working = true; println("clock started."). @plan1 action completion events generated by the environment and execInternalOp("work"); } else { +tick: n_ticks(10) <- stop; failed("already_working"); println("clock stopped."). automatically processed by the agent/environment platform } } @plan2 [atomic] bridge @OPERATION void stop(){ working = false; } +tick: n_ticks(N) <- -+n_ticks(N+1); println("tick perceived!"). no need of explicit observation and reasoning by agents to @INTERNAL_OPERATION void work(){ while (working){ know if an action succeeded signal("tick"); await_time(TICK_TIME); } However the agent execution cycle is not blocked! } } the agent can continue to process percepts and possibly execute actions of other intentions Internal operations execution of operations triggered by other operations implementing controllable processes 67 / 165 68 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Example 4: Artifacts for User I/O – GUI Artifacts Example 4: Agent and User Interaction GUI ARTIFACT USER ASSISTANT AGENT public class MySimpleGUI extends GUIArtifact { !test_gui. private MyFrame frame; +!test_gui public void setup() { <- makeArtifact("gui","MySimpleGUI",Id); frame = new MyFrame(); focus(Id). linkActionEventToOp(frame.okButton,"ok"); +value(V) linkKeyStrokeToOp(frame.text,"ENTER","updateText"); <- println("Value updated: ",V). value 16.0 ok linkWindowClosingEventToOp(frame, "closed"); defineObsProperty("value",getValue()); +ok : value(V) frame.setVisible(true); <- setValue(V+1). } setValue closed +closed @INTERNAL_OPERATION void ok(ActionEvent ev){ <- .my_name(Me); user signal("ok"); .kill_agent(Me). } agent @OPERATION void setValue(double value){ frame.setText(""+value); updateObsProperty("value",value); } ... Exploiting artifacts to enable interaction between human @INTERNAL_OPERATION void updateText(ActionEvent ev){ updateObsProperty("value",getValue()); users and agents } private int getValue(){ return Integer.parseInt(frame.getText()); } class MyFrame extends JFrame {...} } 69 / 165 70 / 165 Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Other Features A&A and CArtAgO: Some Research Explorations Designing and implementing artifact-based organisation Other CArtAgO features not discussed in this lecture Infrastructures linkability JaCaMo model and platform (which is the evolution of the executing chains of operations across multiple artifacts ORA4MAS infrastructure [H¨bner et al., 2009c]) u multiple workspaces Cognitive stigmergy based on artifact agents can join and work in multiple workspaces, concurrently environments [Ricci et al., 2007a] including remote workspaces cognitive artifacts for knowledge representation and RBAC security model coordination [Piunti and Ricci, 2009] workspace artifact provides operations to set/change the Artifact-based environments for access control policies of the workspace, depending on the argumentation [Oliva et al., 2010] agent role ruling agents’ access and use of artifacts of the workspace Including A&A in AOSE methodology [Molesini et al., 2005] ... Defining a Semantic (OWL-based) description of artifact See CArtAgO papers and manuals for more information environments ( CArtAgO-DL) JaSa project = JASDL + CArtAgO-DL ... 71 / 165 72 / 165
  • 13.
    Introduction EOP OOPConclusion Introduction Basic Advanced CArtAgO Wrap-up Introduction EOP OOP Conclusion Introduction Basic Advanced CArtAgO Wrap-up Applying CArtAgO and JaCa Wrap-up Environment programming Using CArtAgO/JaCa for building real-world applications and environment as a programmable part of the MAS infrastructures encapsulating and modularising functionalities useful for Some examples agents’ work JaCa-Android Artifact-based environments implementing mobile computing applications on top of the artifacts as first-class abstraction to design and program Android platform using JaCa [Santi et al., 2011] complex software environments http://jaca-android.sourceforge.net usage interface, observable properties / events, linkability JaCa-WS / CArtAgO-WS artifacts as first-order entities for agents building SOA/Web Services applications using interaction based on use and observation JaCa [Ricci et al., 2010a] agents dynamically co-constructing, evolving, adapting their http://cartagows.sourceforge.net world JaCa-Web CArtAgO computational framework implementing Web 2.0 applications using JaCa programming and executing artifact-based environments http://jaca-web.sourceforge.net integration with heterogeneous agent platforms JaCa case 73 / 165 74 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Abstractions in Multi-Agent Systems schema mission role ORGAMISATION org LEVEL Organisation Oriented agent Programming AGENT LEVEL — OOP — artifact ENDOGENOUS ENVIRONMENT wsp LEVEL EXOGENOUS ENVIRONMENT 76 / 165 network node Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation in MAS – a definition Outline 3 Organisation Oriented Programming (OOP) What is an organisation? Fundamentals Motivations Some OOP approaches Focus on the Moise framework Moise Organisation Modelling Language (OML) Moise Organisation Management Infrastructure (OMI) Moise integration with agents & environment 78 / 165
  • 14.
    Introduction EOP OOPConclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation in MAS – a definition Introduction: Some definitions Organisations are structured, patterned systems of activity, knowledge, culture, memory, history, and capabilities that are distinct from any single agent [Gasser, 2001] What is an organisation? Organisations are supra-individual phenomena A decision and communication schema which is applied to a set of Pattern of agent cooperation actors that together fulfill a set of tasks in order to satisfy goals with a purpose while guarantying a global coherent state [Malone, 1999] supra-agent definition by the designer, or by actors, to achieve a purpose emergent or An organisation is characterised by: a division of tasks, a predefined (by designer or agents) distribution of roles, authority systems, communication systems, contribution-retribution systems [Bernoux, 1985] pattern of predefined cooperation An arrangement of relationships between components, which results into an entity, a system, that has unknown skills at the level of the individuals [Morin, 1977] pattern of emergent cooperation 78 / 165 79 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Perspective on organisations from EASSS’05 Tutorial (Sichman, Boissier) Perspective on organisations from EASSS’05 Tutorial (Sichman, Boissier) Agent Centred Agent Centred Swarms, AMAS, SASO Social Reasoning Self-organisations … Coalition formation Contract Net Protocol … Organisation is observed. Organisation is observed. Implicitly programmed Coalition formation in Agents, Interactions, mechanisms programmed Environment. in Agents. Agents don’t know Agents know Agents don’t know Agents know about organisation about organisation about organisation about organisation AOSE TAEMS, STEAM, AGR MASE, GAIA, MESSAGE, … MOISE+, OPERA, … Organisation is Organisation-Oriented a design model. Programming of MAS It is hard-coded in Agents Organisation Centred Organisation Centred Organisation Specification Designer / Observer Organisation Specification Designer / Observer Organisation Entity Observed Organisation Bottom-up Top-down Organisation Entity Observed Organisation Bottom-up Top-down 80 / 165 81 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation Oriented Programming (OOP) Organisation Oriented Programming (OOP) Programming outside the agents Agent Agent Using organisational concepts Agent To define a cooperative Agent First approach Organisation Organisation Specification pattern Specification Agents read the program and follow it Agent Program = Specification Agent By changing the Organisation specification, we can Organisation Entity change the MAS overall Entity behaviour 82 / 165 82 / 165
  • 15.
    Introduction EOP OOPConclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation Oriented Programming (OOP) Organisation Oriented Programming (OOP) Agent Agent Second approach Second approach Agent Agents are forced to Agent Agents are forced to Organisation follow the program Organisation follow the program Specification Specification Agents are rewarded if Agent Agent they follow the program ... Organisation Organisation Entity Entity 82 / 165 82 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation Oriented Programming (OOP) Components of OOP: Organisation Modelling Language (OML) Agent Declarative specification of the organisation(s) Components Specific constraints, norms and cooperation patterns Programming language imposed on the agents Agent (OML) Organisation Based on an organisational model Specification Platform (OMI) e.g. AGR [Ferber and Gutknecht, 1998], Integration to agent TeamCore [Tambe, 1997], Agent architectures and Islander [Esteva et al., 2001], environment Moise+ [H¨bner et al., 2002], u Organisation Opera [Dignum and Aldewereld, 2010], Entity 2OPL [Dastani et al., 2009a], ... 82 / 165 83 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Components of OOP: Components of OOP: Organisation Management Infrastructure (OMI) Integration mechanisms Agent integration mechanisms allow agents to be aware of and to deliberate on: Coordination mechanisms, i.e. support infrastructure entering/exiting the organisation e.g. MadKit [Gutknecht and Ferber, 2000b], modification of the organisation Karma [Pynadath and Tambe, 2003], obedience/violation of norms ... sanctioning/rewarding other agents Regulation mechanisms, i.e. governance infrastructure e.g. J -Moise+ [H¨bner et al., 2007], Autonomy based u e.g. Ameli [Esteva et al., 2004], reasoning [Carabelea, 2007], ProsA2 Agent-based reasoning S-Moise+ [H¨bner et al., 2006], u on norms [Ossowski, 1999], ... ORA4MAS [H¨bner et al., 2009b], u Environment integration mechanisms ... transform organisation into embodied organisation so that: Adaptation mechanisms, i.e. reorganisation infrastructure organisation may act on the environment (e.g. enact rules, regimentation) environment may act on the organisation (e.g. count-as rules) e.g [Piunti et al., 2009b], [Okuyama et al., 2008] 84 / 165 85 / 165
  • 16.
    Introduction EOP OOPConclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Motivations for OOP: Motivations for OOP: Applications point of view Constitutive point of view Current applications show an increase in Organisation helps the agents to cooperate with other Number of agents agents by defining common cooperation schemes Duration and repetitiveness of agent activities global tasks Heterogeneity of the agents protocols Number of designers of agents groups, responsibilities Agent ability to act and decide e.g. ‘to bid’ for a product on eBay is an institutional action only Openness, scalability, dynamism possible because eBay defines rules for that very action More and more applications require the integration of human the bid protocol is a constraint but it also creates the action communities and technological communities (ubiquitous and pervasive computing), building connected communities e.g. when a soccer team wants to play match, the organisation (ICities) in which agents act on behalf of users helps the members of the team to synchronise actions, to Trust, security, ..., flexibility, adaptation share information, etc 86 / 165 87 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Motivations for OOP: Motivations for OOP: Normative point of view Agents point of view MAS have two properties which seem contradictory: a global purpose autonomous agents An organisational specification is required to enable agents to While the autonomy of the agents is essential, it may cause “reason” about the organisation: loss in the global coherence of the system and achievement of to decide to enter into/leave from the organisation during the global purpose execution Embedding norms within the organisation of an MAS is a Organisation is no more closed way to constrain the agents’ behaviour towards the global to change/adapt the current organisation purposes of the organisation, while explicitly addressing the Organisation is no more static autonomy of the agents within the organisation Normative organisation to obey/disobey the organisation e.g. when an agent adopts a role, it adopts a set of behavioural Organisation is no more a regimentation constraints that support the global purpose of the organisation. It may decide to obey or disobey these constraints 88 / 165 89 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Motivations for OOP: Some OOP approaches Organisation point of view An organisational specification is required to enable the organisation to “reason” about itself and about the agents in AGR/Madkit [Ferber and Gutknecht, 1998] order to ensure the achievement of its global purpose: STEAM/Teamcore [Tambe, 1997] to decide to let agents enter into/leave from the ISLANDER/AMELI [Esteva et al., 2004] organisation during execution Opera/Operetta [Dignum and Aldewereld, 2010] Organisation is no more closed to decide to let agents change/adapt the current PopOrg [Rocha Costa and Dimuro, 2009] organisation 2OPL [Dastani et al., 2009a] Organisation is no more static and blind ... to govern agents behaviour in the organisation (i.e. monitor, enforce, regiment) Organisation is no more a regimentation 90 / 165 91 / 165
  • 17.
    Introduction EOP OOPConclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise AGR [Ferber and Gutknecht, 1998] AGR OML is member of * Agent * Agent Group Role (previously known as AALAADIN) plays 1..* Agent: Active entity that plays roles within groups. An agent Group Agent may have several roles and may belong to several groups level Group: set of agents sharing common characteristics, i.e. 1 context for a set of activities. Two agents cant communicate described by Organization with each other if they dont belong to the same group 1 1..* 1 contains level Role: Abstract representation of the status, position, 1..* participant Group structure Role 1..* function of an agent within a group 1 1 initiator source 1 1 target OMI: the Madkit platform * 1 1 * Interaction Role properties Role dependency protocol * 92 / 165 93 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise AGR OML Modelling Dimensions STEAM [Tambe, 1997] Environment Shell for TEAMwork is a general framework to enable agents to participate in teamwork P Di↵erent applications: Attack, Transport, Robocup soccer E Based on an enhanced SOAR architecture and 300 domain OS independent SOAR rules Principles: Team synchronisation: Establish joint intentions, Monitor team progress and repair, Individual may fail or succeed in Structural Specification B own role Reorganise if there is a critical role failure B: agents’ possible behaviors Reassign critical roles based on joint intentions P: agents’ behaviors that lead to global purpose Decision theoretic communication E: agents’ possible behaviors constrained by the environment OS: agents’ possible behaviors structurally constrained by the organization Supported by the TEAMCORE OMI 94 / 165 95 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise STEAM OML [Tambe, 1997] STEAM OML Modelling Dimensions TASK FORCE EVACUATE Environment [TASK FORCE] P PROCESS ORDERS EXECUTE LANDING ZONE E ORDERS SAFETY INFO FLIGHT ROUTE MISSION [TASK FORCE] [TASK FORCE] MANEUVERS OBTAINER OBTAINER TEAM PLANNER [TASK FORCE] OS ESCORT TRANSPORT OBTAIN FLY-FLIGHT MASK PICKUP ORDERS PLAN OBSERVE [TRANSPORT] [ORDERS [TASK FORCE] [ESCORT] OBTAINER] Structural OF Functional HELO1 HELO2 HELO1 HELO2 Specification B Specification FLY-CONTROL ROUTE [TASK FORCE] B: agents’ possible behaviors Organization: hierarchy of roles that P: agents’ behaviors that lead to global purpose may be filled by agents or groups of Team Plan: E: agents’ possible behaviors constrained by the environment agents. •  initial conditions, OS: agents’ possible behaviors structurally constrained by the organization •  term. cond. : achievability, irrelevance, OF: agents’ possible behaviors functionally constrained by the organization unachievability •  team-level actions. 96 / 165 97 / 165
  • 18.
    Introduction EOP OOPConclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise ISLANDER ISLANDER OML: IDL [Esteva et al., 2001] (define-institution soccer-server as Based on di↵erent influences: economics, norms, dialogues, dialogic-framework = soccer-df coordination performative-structure = soccer-pf norms = ( free-kick coach-messages … ) electronic institutions ) Combining di↵erent alternative views: dialogical, normative, coordination Institution Description Language: Performative structure (Network of protocols) Scene (multi-agent protocol) Roles Norms Ameli as OMI Performative Structure 98 / 165 99 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise ISLANDER OML Modelling Dimensions 2OPL slides from Dastani Environment The aim is to design and develop a programming language to P E support the implementation of coordination mechanisms in terms of normative concepts. OS An organisation OI determines e↵ect of external actions Structural Dialogical Specification B Specification normatively assesses e↵ect of agents’ actions (monitoring) B: agents’ possible behaviors sanctions agents’ wrongdoings (enforcement) P: agents’ behaviors that lead to global purpose prevents ending up in really bad states (regimentation) E: agents’ possible behaviors constrained by the environment OS: agents’ possible/permitted/obliged behaviors structurally constrained by the organisation OI: agents’ possible/permitted/obliged behaviors interactionally constrained by the organisation 100 / 165 101 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Programming Language for Organisations 2OPL Modelling Dimension Example (Train Station) Facts: – -at˙platform , -in˙train , -ticket ˝ Effects: – -at˙platform ˝ enter – at˙platform ˝, – -ticket ˝ buy˙ticket – ticket ˝, – at˙platform , -in˙train ˝ embark – -at˙platform, in˙train ˝ Counts˙as rules: – at˙platform , -ticket ˝ =¿ – viol˙ticket ˝, – in˙train , -ticket ˝ =¿ – viol˙—˙ ˝ Sanction˙rules: – viol˙ticket ˝ =¿ – fined˙10 ˝ 102 / 165 103 / 165
  • 19.
    Introduction EOP OOPConclusion Introduction Motivations OOP Moise Summary Several models Several dimensions on modelling organisation The Moise Framework Structural (roles, groups, ...) Functional (global plans, ....) Dialogical (scenes, protocols, ...) Normative (norms) 104 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Moise Framework Moise Modelling Dimensions OML (language) Environment Tag-based language (issued from Moise [Hannoun et al., 2000], P Moise+ [H¨bner et al., 2002], u E MoiseInst [Gˆteau et al., 2005]) a OMI (infrastructure) OS developed as an artifact-based working environment (ORA4MAS [H¨bner et al., 2009b] based on CArtAgO u nodes, refactoring of S-Moise+ [H¨bner et al., 2006] and u Structural OF Functional Specification B Specification Synai [Gˆteau et al., 2005]) a Integrations Groups, links, roles Global goals, plans, Compatibilities, multiplicities Missions, schemas, Agents and Environment (c4Jason, c4Jadex Normative Specification preferences inheritance [Ricci et al., 2009c]) Permissions, Obligations Environment and Organisation ([Piunti et al., 2009a]) Allows agents autonomy! Agents and Organisation (J -Moise+ [H¨bner et al., 2007]) u 106 / 165 107 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Moise OML meta-model (partial view) Moise OML Agent Goal OML for defining organisation specification and organisation entity Structural Specification create delete Three independent dimensions [H¨bner et al., 2007] u create delete ( well adapted for the reorganisation concerns): Normative Social Scheme Specification Structural: Roles, Groups Group achieve Functional Specification Goal Functional: Goals, Missions, Schemes adopt leave Normative: Norms (obligations, permissions, interdictions) commit Role Norm Mission leave Abstract description of the organisation for the designers the agents composition agent's actions J -Moise+ [H¨bner et al., 2007] u association concept mapping the Organisation Management Infrastructure Cardinalities are not represented ORA4MAS [H¨bner et al., 2009b] u 108 / 165 109 / 165
  • 20.
    Introduction EOP OOPConclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Moise OML Structural Specification Structural Specification Example Specifies the structure of an MAS along three levels: Individual with Role Social with Link Collective with Group Components: Role: label used to assign rights and constraints on the behavior of agents playing it Link: relation between roles that directly constrains the agents in their interaction with the other agents playing the corresponding roles Group: set of links, roles, compatibility relations used to define a shared context for agents playing roles in it Graphical representation of structural specification of 3-5-2 Joj Team 110 / 165 111 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Moise OML Functional Specification Functional Specification Example Specifies the expected behaviour of an MAS in terms of goals along two levels: m1, m2, m3 score a goal Collective with Scheme Individual with Mission m1 m3 Components: get the ball shot at the opponent’s goal Goals: m1 m2 Achievement goal (default type). Goals of this type should go towards the opponent field kick the ball to the goal area be declared as satisfied by the agents committed to them, m2 m2 be placed in the middle field go to the opponent back line when achieved m1 Maintenance goal. Goals of this type are not satisfied at a m3 kick the ball to (agent committed to m2) precise moment but are pursued while the scheme is running. be placed in the opponent goal area The agents committed to them do not need to declare that Key Organizational Entity they are satisfied Scheme Lucio m1 Scheme: global goal decomposition tree assigned to a group missions Cafu m2 goal sequence choice parallelism Any scheme has a root goal that is decomposed into subgoals success rate Rivaldo m3 Missions: set of coherent goals assigned to roles within Graphical representation of social scheme “side attack” for joj team norms 112 / 165 113 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Goal States Moise OML Normative Specification waiting enabled Explicit relation between the functional and structural specifications impossible satisfied Permissions and obligations to commit to missions in the context of a role Makes explicit the normative dimension of a role waiting initial state enabled goal pre-conditions are satisfied & scheme is well-formed satisfied agents committed to the goal have achieved it impossible the goal is impossible to be satisfied 114 / 165 115 / 165
  • 21.
    Introduction EOP OOPConclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Norm Specification – example Organisational Entity structural norrmative functional groups schemas Organisation links norms roles missions specification role deontic mission TTF back obliged m1 get the ball, go ... 1 minute purpose left obliged m2 be placed at ..., kick ... 3 minute right obliged m2 1 day attacker obliged m3 kick to the goal, ... 30 seconds group schema instances role instances mission Organisation player player Entity agents 116 / 165 117 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisation Entity Dynamics Organisation management infrastructure (OMI) Responsibility 1 Organisation is created (by the agents) Managing – coordination, regulation – the agents’ execution instances of groups within organisation defined in an organisational specification instances of schemes 2 Agents enter into groups adopting roles 3 Groups become responsible for schemes Agent Agent Agent Agent Agents from the group are then obliged to commit to missions in the scheme 4 Agents commit to missions 5 Agents fulfil mission’s goals Organisation OMI Specification 6 Agents leave schemes and groups 7 Schemes and groups instances are destroyed (e.g. MadKit, AMELI, S-Moise+ , ...) 118 / 165 119 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisational artifacts in ORA4MAS ORA4MAS – GroupBoard artifact System GroupBoard based on A&A and Agent Moise Specification Observable Properties: Agent agents create and handle Players specification: the organisational artifacts specification of the group in Agent Agent Schemes the OS (an object of class Group artifacts in charge of Artifact Formation status moise.os.ss.Group) regimentations, detection and evaluation players: a list of agents Scheme of norms compliance playing roles in the group. Artifact adoptRole Each element of the list is a agents are in charge of leaveRole pair (agent x role) Scheme Agent decisions about Artifact Agent sanctions addScheme schemes: a list of scheme identifiers that the group is distributed solution removeScheme responsible for 120 / 165 121 / 165
  • 22.
    Introduction EOP OOPConclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise ORA4MAS – GroupBoard artifact ORA4MAS – SchemeBoard artifact GroupBoard SchemeBoard Observable Properties: Specification Operations: Specification specification: the specification of the scheme adoptRole(role): the agent Groups Players in the OS executing this operation Schemes tries to adopt a role in the Players groups: a list of groups group Goals responsible for the scheme Formation status leaveRole(role) Obligations players: a list of agents committed to the scheme. addScheme(schid): the adoptRole Each element of the list is a group starts to be commitMission pair (agent, mission) leaveRole responsible for the scheme leaveMission goals: a list with the managed by the addScheme goalAchieved current state of the goals SchemeBoard schId removeScheme removeScheme(schid) setGoalArgument obligations: list of obligations currently active in the scheme 122 / 165 123 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise ORA4MAS – SchemeBoard artifact Organisational Artifact Architecture SchemeBoard Organisation Operations: Group State Artifact Specification commitMission(mission) Groups NPL Interpreter and leaveMission: NPL Engine Players operations to “enter” and NOPL translates Organisation Program Specificatoin Goals “leave” the scheme Obligations goalAchieved(goal): defines State Obligations that some goal is achieved by the agent performing the commitMission operation Signals (o = obligation(to whom, reason, what, deadline)): leaveMission setGoalArgument(goal, obl created(o): the obligation o is created goalAchieved argument, value): defines obl fulfilled(o): the obligation o is fulfilled the value of some goal’s obl unfulfilled(o): the obligation o is unfulfilled setGoalArgument argument obl inactive(o): the obligation o is inactive norm failure(f ): the failure f has happened 124 / 165 125 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Environment integration Environment integration: constitutive rules Count-As rule Organisational Artifacts enable organisation and environment An event occurring on an artifact, in a particular context, may integration count-as an institutional event Embodied organisation [Piunti et al., 2009a] transforms the events created in the working environment into activation of an organisational operation count-as indirect automatic updating of the organisation count-as Enact rule Env. Artifact Org. Artifact An event produced on an organisational artifact, in a specific institutional context, may “enact” change and updating of the working environment (i.e., to promote equilibrium, avoid enact undesiderable states) Installing automated control on the working environment Even without the intervention of organisational/sta↵ agents status: ongoing work (regimenting actions on physical artifacts, enforcing 126 / 165 sanctions, ...) 127 / 165
  • 23.
    Introduction EOP OOPConclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Agent integration J -Moise: Jason + Moise Agents can interact with organisational artifacts as with Agents are programmed with Jason ordinary artifacts by perception and action BDI agents (reactive planning) – suitable abstraction level Any Agent Programming Language integrated with CArtAgO can use organisational artifacts The programmer has the possibility to express sophisticated recipes for adopting roles, committing to missions, fulfilling/violating norms, ... Agent integration provides some “internal” tools for the agents Organisational information is made accessible in the mental to simplify their interaction with the organisation: state of the agent as beliefs maintenance of a local copy of the organisational state Integration is totally independent of the production of organisational events distribution/communication layer provision of organisational actions 128 / 165 129 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise J -Moise: Jason + Moise– General view Organisational actions in Jason I Example (GroupBoard) ... Jason-CArtAgo Agent joinWorkspace(”ora4mas”,O4MWsp); makeArtifact( Belief Plan ”auction”, Intentions Base Library ”ora4mas.nopl.GroupBoard”, [”auction-os.xml”, auctionGroup, false, true ], GrArtId); J-Moise+ adoptRole(auctioneer); focus(GrArtId); ... Organisational Workspace (CArtAgO) 130 / 165 131 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisational actions in Jason II Organisational perception Example (SchemeBoard) ... makeArtifact( When an agent focus on an Organisational Artifact, the ”sch1”, observable properties (Java objects) are translated to beliefs with ”ora4mas.nopl.SchemeBoard”, the following predicates: [”auction-os.xml”, doAuction, false, true ], specification SchArtId); focus(SchArtId); play(agent, role, group) addScheme(Sch); commitment(agent, mission, scheme) commitMission(mAuctioneer)[artifact˙id(SchArtId)]; goalState(scheme, goal, list of committed agents, list of ... agent that achieved the goal, state of the goal) obligation(agent,norm,goal,dead line) .... 132 / 165 133 / 165
  • 24.
    Introduction EOP OOPConclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Organisational perception – example Handling organisational events in Jason Whenever something changes in the organisation, the agent architecture updates the agent belief base accordingly producing events (belief update from perception) Example (new agent entered the group) +play(Ag,boss,GId) ¡- .send(Ag,tell,hello). Example (change in goal state) +goalState(Scheme,wsecs,˙,˙,satisfied) : .my˙name(Me) & commitment(Me,mCol,Scheme) ¡- leaveMission(mColaborator,Scheme). Example (signals) +normFailure(N) ¡- .print(”norm failure event: ”, N). 134 / 165 135 / 165 Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Introduction EOP OOP Conclusion Introduction Motivations OOP Moise Typical plans for obligations Summary – Moise Example Ensures that the agents follow some of the constraints +obligation(Ag,Norm,committed(Ag,Mission,Scheme),DeadLine) : .my˙name(Ag) specified for the organisation ¡- .print(”I am obliged to commit to ”,Mission); Helps the agents to work together commit˙mission(Mission,Scheme). The organisation is interpreted at runtime, it is not hardwired in the agents code +obligation(Ag,Norm,achieved(Sch,Goal,Ag),DeadLine) : .my˙name(Ag) The agents ‘handle’ the organisation (i.e. their artifacts) ¡- .print(”I am obliged to achieve goal ”,Goal); It is suitable for open systems as no specific agent !Goal[scheme(Sch)]; architecture is required goal˙achieved(Goal,Sch). +obligation(Ag,Norm,What,DeadLine) All available as open source at : .my˙name(Ag) ¡- .print(”I am obliged to ”,What, http://moise.souceforge.net ”, but I don’t know what to do!”). 136 / 165 137 / 165 Introduction EOP OOP Conclusion Putting the Pieces Together INTERNAL BELIEFS GROUPS ROLES EVENTS GOALS SANCTIONS PLANS MISSIONS REWARDS ACTIONS DEONTIC RELATIONS Conclusions PERCEPTIONS NORMS JASON MOISE ? AGENTS ORGANISATIONS Agent Prog. Framework Language CarTaGO JADE Platform Platform SPEECH COMMUNICATION RESOURCES ACTS LANGUAGES LEGACY INTERACTION SERVICES OBJECTS PROCOLS ENVIRONMENTS INTERACTIONS 139 / 165
  • 25.
    Introduction EOP OOPConclusion Introduction EOP OOP Conclusion Exploiting Orthogonality Beyond Orthogonality: Synergetic Integration Treating AOP & EOP & OOP as orthogonal dimensions improving separation of concerns using the best abstraction level and tools to tackle the Exploiting one dimension to e↵ectively design and program specific dimensions, avoiding design pitfalls, such as using agents to implement either non-autonomous entities (e.g., a also aspects related to the other dimensions blackboard agent) or a collection of autonomous entities for instance, using the environment to design, implement and (group agent) represent at runtime the organisation infrastructure promoting openness and heterogeneity Designing and implementing MAS behaviours that are based E.g., heterogeneous agents working in the same organisation, on explicit bindings between the di↵erent dimensions heterogeneous agents working in the same environment, the for instance, exploiting events occurring in the environment to same agent working in di↵erent and heterogeneous represent events that have an e↵ect at the institutional or organisations, the same agent working in di↵erent heterogeneous environments social level Outcome from a programming point of view code more clean and understandable improving modularity, extensibility, reusability 140 / 165 141 / 165 Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion Exploiting Synergy between the A/E Dimensions Exploiting Synergy on A/O Integration Normative deliberative agents Mapping possibility to define mechanisms for agents to evolve within agent actions into environment operations (e.g. CArtAgO) an organisation/several organisations environment observable state/events into agent beliefs possibility to define proper mechanisms for deliberating on the Outcome internalisation/adoption/violation of norms agents with dynamic action repertoire Reorganisation, adaptation of the organisation uniformly implementing any mechanisms (e.g. coordination possibility to define proper mechanisms for mechanism) in terms of actions/percepts diagnosing/evaluating/refining/defining organisations no need to extend agents with special purpose primitives “Deliberative” Organisations exploiting a new type of agent modularity, based on possibility to define dedicated organisational strategies for the externalization [Ricci et al., 2009a] regulation/adaptation of the organisation behaviour (organisational agents) 142 / 165 143 / 165 Introduction EOP OOP Conclusion Introduction EOP OOP Conclusion Exploiting Synergy between the E/O Dimensions E/O Synergy Example: Implementing Regimentation Grounding the organisation infrastructure implemented using environment abstractions Exploiting the environment role of enabler and mediator of ... that agents perceive then as first-class entities of their agent interaction world by providing actions and generating percepts Mapping ! natural place where to embed and enforce organisational state reified by the environment computational organisational rules and norms state a↵ecting action execution behaviour and percepts generation organisational actions/perceptions reified by actions/percepts Examples on the environment state simple: a game-board artifact in an artifact-based organisational functionalities encapsulated by suitably environment designed environment abstractions providing agents actions to make moves Outcome encapsulating and enforcing the rules of the game “the power is back to agents” [H¨bner et al., 2009c] u complex: fully-fledged institutions by perceiving and acting upon that environment, agents can reified into properly programmed environments reason and dynamically adapt the organisation infrastructure 144 / 165 145 / 165
  • 26.
    Introduction EOP OOPConclusion Introduction EOP OOP Conclusion E/O Synergy Example: Implementing Constitutive Rules A proposal: JaCaMo Environment Manual consult External Action Internal Action dimension Exploiting the environment to create, represent, and manage has create dependencies and rules that are meaningful at the Workspace Artifact dispose link Action Agent join dimension organisational level quit A main example: implementing constitutive Work Environment Operation use Agent Plan rules [Searle, 1997] Observable Property update generate Observable Event Trigger event events occurring in concrete environments conveyed as social Belief and institutional events perceive Goal typically represented in the form X counts as Y in C create create delete an example: reaching the environment state S counts as delete Social Scheme achieving the organisational goal G Group Goal commit adopt The integration E/O allows for naturally design and leave leave implementation of these kinds of rules Organisation dimension Role Norm Mission without adding any further concepts wrt the ones belonging to the E/O dimensions composition agent's actions Legend association dimension border dependency concept mapping 146 / 165 147 / 165 Introduction EOP OOP Conclusion Ongoing and Related Research Unifying agents, environments and organisation perspectives Volcano platform [Ricordel and Demazeau, 2002] MASK platform [Occello et al., 2004] MASQ [Stratulat et al., 2009], extending AGRE and AGREEN Embodied organisations [Piunti, 2010] Situated E-Institutions [Campos et al., 2009] Normative programming and infrastructures [H¨bner et al., 2009a, u Tinnemeier et al., 2009, Dastani et al., 2009b] 148 / 165
  • 27.
    References [H¨ bner et al., 2009a] H¨ bner, J. F., Boissier, O., and Bordini, R. H. u u (2009a). Normative programming for organisation management infras- [Behrens et al., 2010] Behrens, T., Bordini, R., Braubach, L., Dastani, M., tructures. In MALLOW Workshop on Coordination, Organization, Dix, J., Hindriks, K., Hbner, J., and Pokahr, A. (2010). An interface Institutions and Norms in Agent Systems in Online Communities for agent-environment interaction. In In Proceedings of International (COIN-MALLOW 2009). Workshop on Programming Multi-Agent Systems (ProMAS-8). [H¨ bner et al., 2009b] H¨ bner, J. F., Boissier, O., Kitio, R., and Ricci, A. u u [Bernoux, 1985] Bernoux, P. (1985). La sociologie des organisations. (2009b). Instrumenting multi-agent organisations with organisational Seuil, 3`me edition. e artifacts and agents: “Giving the organisational power back to the agents”. Autonomous Agents and Multi-Agent Systems. DOI-URL: [Bordini et al., 2007] Bordini, R., H¨ bner, J., and Wooldridge, M. (2007). u http://dx.doi.org/10.1007/s10458-009-9084-y. Programming Multi-Agent Systems in AgentSpeak Using Jason. Wiley- Interscience. [H¨ bner et al., 2009c] H¨ bner, J. F., Boissier, O., Kitio, R., and Ricci, A. u u (2009c). Instrumenting Multi-Agent Organisations with Organisational [Bromuri and Stathis, 2008] Bromuri, S. and Stathis, K. (2008). Situating Artifacts and Agents. Journal of Autonomous Agents and Multi-Agent Cognitive Agents in GOLEM. In Weyns, D., Brueckner, S., and De- Systems. mazeau, Y., editors, Engineering Environment-Mediated Multi-Agent [H¨ bner et al., 2002] H¨ bner, J. F., Sichman, J. S., and Boissier, O. u u Systems, volume 5049 of LNCS, pages 115–134. Springer Berlin / Hei- (2002). A model for the structural, functional, and deontic specifica- delberg. tion of organizations in multiagent systems. In Bittencourt, G. and Ra- malho, G. L., editors, Proceedings of the 16th Brazilian Symposium on [Campos et al., 2009] Campos, J., L´pez-S´nchez, M., Rodriguez-Aguilar, o a Artificial Intelligence (SBIA’02), volume 2507 of LNAI, pages 118–128, J. A., and Esteva, M. (2009). Formalising situatedness and adaptation Berlin. Springer. in electronic institutions. In Coordination, Organizations, Institutions and Norms in Agent Systems IV, volume 5428/2009 of LNCS. Springer [H¨ bner et al., 2006] H¨ bner, J. F., Sichman, J. S., and Boissier, O. u u Berlin / Heidelberg. (2006). S-MOISE+: A middleware for developing organised multi-agent systems. In Boissier, O., Dignum, V., Matson, E., and Sichman, J. S., ed- [Carabelea, 2007] Carabelea, C. (2007). Reasoning about autonomy in itors, Coordination, Organizations, Institutions, and Norms in Multi- open multi-agent systems - an approach based on the social power the- Agent Systems, volume 3913 of LNCS, pages 64–78. Springer. ory. in french, ENS Mines Saint-Etienne. [H¨ bner et al., 2007] H¨ bner, J. F., Sichman, J. S., and Boissier, O. u u [Dastani, 2008] Dastani, M. (2008). 2APL: a practical agent programming (2007). Developing Organised Multi-Agent Systems Using the MOISE+ language. Autonomous Agent and Multi-Agent Systems, 16(3):214–248. Model: Programming Issues at the System and Agent Levels. Agent- Oriented Software Engineering, 1(3/4):370–395. [Dastani et al., 2009] Dastani, M., Tinnemeier, N., and Meyer, J.-J. C. (2009). A programming language for normative multi-agent systems. [Malone, 1999] Malone, T. W. (1999). Tools for inventing organizations: In Multi-Agent Systems: Semantics and Dynamics of Organizational Toward a handbook of organizational process. Management Science, Models. IGI-Global. 45(3):425–443. [Dignum and Aldewereld, 2010] Dignum, V. and Aldewereld, H. (2010). [Molesini et al., 2005] Molesini, A., Omicini, A., Denti, E., and Ricci, A. Operetta: Organization-oriented development environment. In Proceed- (2005). SODA: A roadmap to artefacts. In Dikenelli, O., Gleizes, M.- ings of LADS @ MALLOW 2010, pages 14–20. P., and Ricci, A., editors, 6th International Workshop “Engineering Societies in the Agents World” (ESAW’05), pages 239–252, Ku¸adası,s Aydın, Turkey. Ege University. [Esteva et al., 2001] Esteva, M., Rodriguez-Aguiar, J. A., Sierra, C., Gar- cia, P., and Arcos, J. L. (2001). On the formal specification of elec- [Morin, 1977] Morin, E. (1977). La m´thode (1) : la nature de la nature. e tronic institutions. In Dignum, F. and Sierra, C., editors, Proceedings Points Seuil. of the Agent-mediated Electronic Commerce, LNAI 1191, pages 126– 147, Berlin. Springer. [Occello et al., 2004] Occello, M., Baeijs, C., Demazeau, Y., and Koning, J.-L. (2004). MASK: An AEIO toolbox to design and build multi-agent [Esteva et al., 2004] Esteva, M., Rodr´ıguez-Aguilar, J. A., Rosell, B., and systems. In et al., C., editor, Knowledge Engineering and Agent Tech- L., J. (2004). AMELI: An agent-based middleware for electronic insti- nology, IOS Series on Frontiers in AI and Applications. IOS press, Am- tutions. In Jennings, N. R., Sierra, C., Sonenberg, L., and Tambe, M., sterdam. editors, Proc. of the 3rd Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems (AAMAS’04), pages 236–243, New York, USA. [Okuyama et al., 2008] Okuyama, F. Y., Bordini, R. H., and ACM. da Rocha Costa, A. C. (2008). A distributed normative infras- tructure for situated multi-agent organisations. In Baldoni, M., Son, [Ferber and Gutknecht, 1998] Ferber, J. and Gutknecht, O. (1998). A T. C., van Riemsdijk, M. B., and Winiko , M., editors, DALT, volume meta-model for the analysis and design of organizations in multi-agents 5397 of Lecture Notes in Computer Science, pages 29–46. Springer. systems. In Demazeau, Y., editor, Proceedings of the 3rd Interna- tional Conference on Multi-Agent Systems (ICMAS’98), pages 128– [Oliva et al., 2010] Oliva, E., McBurney, P., Omicini, A., and Viroli, M. 135. IEEE Press. (2010). Argumentation and artifacts for negotiation support. Interna- tional Journal of Artificial Intelligence, 4(S10):90–117. Special Issue [Gasser, 2001] Gasser, L. (2001). Organizations in multi-agent systems. on Negotiation and Argumentation in Artificial Intelligence. In Pre-Proceeding of the 10th European Worshop on Modeling Au- tonomous Agents in a Multi-Agent World (MAAMAW’2001), Annecy. [Omicini et al., 2008] Omicini, A., Ricci, A., and Viroli, M. (2008). Ar- tifacts in the A&A meta-model for multi-agent systems. Autonomous [Gˆteau et al., 2005] Gˆteau, B., Boissier, O., Khadraoui, D., and Dubois, a a Agents and Multi-Agent Systems, 17(3):432–456. E. (2005). Moiseinst: An organizational model for specifying rights and duties of autonomous agents. In Third European Workshop on Multi- [Omicini et al., 2004] Omicini, A., Ricci, A., Viroli, M., Castelfranchi, C., Agent Systems (EUMAS 2005), pages 484–485, Brussels Belgium. and Tummolini, L. (2004). Coordination artifacts: Environment-based coordination for intelligent agents. In Proc. of the 3rd Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems (AAMAS’04), vol- [Gutknecht and Ferber, 2000a] Gutknecht, O. and Ferber, J. (2000a). The ume 1, pages 286–293, New York, USA. ACM. MADKIT agent platform architecture. In Agents Workshop on Infras- tructure for Multi-Agent Systems, pages 48–55. [Ossowski, 1999] Ossowski, S. (1999). Co-ordination in Artificial Agent Societies: Social Structures and Its Implications for Autonomous [Gutknecht and Ferber, 2000b] Gutknecht, O. and Ferber, J. (2000b). The Problem-Solving Agents, volume 1535 of LNAI. Springer. MadKit agent platform architecture. In Agents Workshop on Infras- tructure for Multi-Agent Systems, pages 48–55. [Piunti, 2010] Piunti, M. (2010). Embodied organizations: a unifying per- spective in programming agents, organizations and environments. PhD [Hannoun et al., 2000] Hannoun, M., Boissier, O., Sichman, J. S., and thesis. Sayettat, C. (2000). Moise: An organizational model for multi-agent systems. In Monard, M. C. and Sichman, J. S., editors, Proceedings of [Piunti and Ricci, 2009] Piunti, M. and Ricci, A. (2009). Cognitive ar- the International Joint Conference, 7th Ibero-American Conference on tifacts for intelligent agents in mas: Exploiting relevant information AI, 15th Brazilian Symposium on AI (IBERAMIA/SBIA’2000), At- residing in environments. In Knowledge Representation for Agents ibaia, SP, Brazil, November 2000, LNAI 1952, pages 152–161, Berlin. and Multi-Agent Systems (KRAMAS 2008), volume 5605 of LNAI. Springer. Springer. 1
  • 28.
    [Piunti et al.,2009a] Piunti, M., Ricci, A., Boissier, O., and Hubner, J. [Ricordel and Demazeau, 2002] Ricordel, P. and Demazeau, Y. (2002). (2009a). Embodying organisations in multi-agent work environments. In VOLCANO: a vowels-oriented multi-agent platform. In Dunin-Keplicz IEEE/WIC/ACM International Conference on Web Intelligence and and Nawarecki, editors, Proceedings of the International Conference Intelligent Agent Technology (WI-IAT 2009), Milan, Italy. of Central Eastern Europe on Multi-Agent Systems (CEEMAS’01), volume 2296 of LNAI, pages 252–262. Springer Verlag. [Piunti et al., 2009b] Piunti, M., Ricci, A., Boissier, O., and H¨ bner, J. F. u (2009b). Embodied organisations in mas environments. In Braubach, [Rocha Costa and Dimuro, 2009] Rocha Costa, A. C. d. and Dimuro, G. L., van der Hoek, W., Petta, P., and Pokahr, A., editors, Proceedings of (2009). A minimal dynamical organization model. In Dignum, V., edi- 7th German conference on Multi-Agent System Technologies (MATES tor, Multi-Agent Systems: Semantics and Dynamics of Organizational 09), Hamburg, Germany, September 9-11, volume 5774 of LNCS, pages Models, chapter XVII, pages 419–445. IGI Global. 115–127. Springer. [Russell and Norvig, 2003] Russell, S. and Norvig, P. (2003). Artificial [Piunti et al., 2008] Piunti, M., Ricci, A., Braubach, L., and Pokahr, Intelligence, A Modern Approach (2nd ed.). Prentice Hall. A. (2008). Goal-directed interactions in artifact-based mas: Jadex agents playing in CARTAGO environments. In Proc. of the 2008 [Santi et al., 2011] Santi, A., Guidi, M., and Ricci, A. (2011). Jaca- IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent android: An agent-based platform for building smart mobile applica- Agent Technology (IAT’08), volume 2. IEEE Computer Society. tions. In Languages, Methodologies and Development Tools for Multi- agent systems, volume 6822 of LNAI. Springer Verlag. [Platon et al., 2007] Platon, E., Mamei, M., Sabouret, N., Honiden, S., [Searle, 1997] Searle, J. R. (1997). The Construction of Social Reality. and Parunak, H. V. (2007). Mechanisms for environments in multi-agent Free Press. systems: Survey and opportunities. Autonomous Agents and Multi- Agent Systems, 14(1):31–47. [Shoham, 1993] Shoham, Y. (1993). Agent-oriented programming. Artif. Intell., 60(1):51–92. [Pynadath and Tambe, 2003] Pynadath, D. V. and Tambe, M. (2003). An automated teamwork infrastructure for heterogeneous software agents [Stratulat et al., 2009] Stratulat, T., Ferber, J., and Tranier, J. (2009). and humans. Autonomous Agents and Multi-Agent Systems, 7(1-2):71– MASQ: towards an integral approach to interaction. In AAMAS (2), 100. pages 813–820. [Ricci et al., 2010a] Ricci, A., Denti, E., and Piunti, M. (2010a). A plat- [Tambe, 1997] Tambe, M. (1997). Towards flexible teamwork. Journal of form for developing SOA/WS applications as open and heterogeneous Artificial Intelligence Reseearch, 7:83–124. multi-agent systems. Multiagent and Grid Systems International Jour- nal (MAGS), Special Issue about “Agents, Web Services and Ontolo- [Tinnemeier et al., 2009] Tinnemeier, N., Dastani, M., Meyer, J.-J., and gies: Integrated Methodologies”. To Appear. van der Torre, L. (2009). Programming normative artifacts with declar- ative obligations and prohibitions. In IEEE/WIC/ACM International [Ricci et al., 2007a] Ricci, A., Omicini, A., Viroli, M., Gardelli, L., and Joint Conference on Web Intelligence and Intelligent Agent Technol- Oliva, E. (2007a). Cognitive stigmergy: Towards a framework based ogy (WI-IAT 2009). on agents and artifacts. In Weyns, D., Parunak, H. V. D., and Michel, F., editors, Environments for MultiAgent Systems III, volume 4389 of [Viroli et al., 2007] Viroli, M., Holvoet, T., Ricci, A., Schelfthout, K., and LNAI, pages 124–140. Springer. Zambonelli, F. (2007). Infrastructures for the environment of multiagent systems. Autonomous Agents and Multi-Agent Systems, 14(1):49–60. [Ricci et al., 2008] Ricci, A., Piunti, M., Acay, L. D., Bordini, R., H¨ bner, u J., and Dastani, M. (2008). Integrating artifact-based environments [Weyns and Holvoet, 2004] Weyns, D. and Holvoet, T. (2004). A formal with heterogeneous agent-programming platforms. In Proceedings of model for situated multi-agent systems. Fundamenta Informaticae, 7th International Conference on Agents and Multi Agents Systems 63(2-3):125–158. (AAMAS08). [Weyns and Holvoet, 2007] Weyns, D. and Holvoet, T. (2007). A reference architecture for situated multiagent systems. In Environments for Mul- [Ricci et al., 2009a] Ricci, A., Piunti, M., and Viroli, M. (2009a). Ex- tiagent Systems III, volume 4389 of LNCS, pages 1–40. Springer Berlin ternalisation and internalization: A new perspective on agent modu- / Heidelberg. larisation in multi-agent system programming. In Dastani, M., Fallah- Seghrouchni, A. E., Leite, J., and Torroni, P., editors, LADS, volume [Weyns et al., 2007] Weyns, D., Omicini, A., and Odell, J. J. (2007). Envi- 6039 of Lecture Notes in Computer Science, pages 35–54. Springer. ronment as a first-class abstraction in multi-agent systems. Autonomous Agents and Multi-Agent Systems, 14(1):5–30. [Ricci et al., 2010b] Ricci, A., Piunti, M., and Viroli, M. (2010b). Environ- ment programming in multi-agent systems – an artifact-based perspec- [Weyns and Parunak, 2007] Weyns, D. and Parunak, H. V. D., editors tive. Autonomous Agents and Multi-Agent Systems. Published Online (2007). Special Issue on Environments for Multi-Agent Systems, vol- with ISSN 1573-7454 (will appear with ISSN 1387-2532). ume 14 (1) of Autonomous Agents and Multi-Agent Systems. Springer Netherlands. [Ricci et al., 2009b] Ricci, A., Piunti, M., Viroli, M., and Omicini, A. (2009b). Environment programming in CArtAgO. In Bordini, R. H., Das- [Weyns et al., 2005] Weyns, D., Parunak, H. V. D., Michel, F., Holvoet, tani, M., Dix, J., and El Fallah-Seghrouchni, A., editors, Multi-Agent T., and Ferber, J. (2005). Environments for multiagent systems: State- Programming: Languages, Platforms and Applications, Vol. 2, pages of-the-art and research challenges. In Weyns, D., Parunak, H. V. D., 259–288. Springer Berlin / Heidelberg. Michel, F., Holvoet, T., and Ferber, J., editors, Environment for Multi- Agent Systems, volume 3374, pages 1–47. Springer Berlin / Heidelberg. [Ricci et al., 2009c] Ricci, A., Piunti, M., Viroli, M., and Omicini, A. (2009c). Environment programming in CArtAgO. In Multi-Agent Pro- [Wooldridge, 2002] Wooldridge, M. (2002). An Introduction to Multi- gramming: Languages,Platforms and Applications,Vol.2. Springer. Agent Systems. John Wiley & Sons, Ltd. [Ricci et al., 2010c] Ricci, A., Santi, A., and Piunti, M. (2010c). Action and perception in multi-agent programming languages: From exogenous to endogenous environments. In In Proceedings of International Work- shop on Programming Multi-Agent Systems (ProMAS-8). [Ricci et al., 2007b] Ricci, A., Viroli, M., and Omicini, A. (2007b). The A&A programming model & technology for developing agent environ- ments in MAS. In Dastani, M., El Fallah Seghrouchni, A., Ricci, A., and Winiko , M., editors, Programming Multi-Agent Systems, volume 4908 of LNAI, pages 91–109. Springer Berlin / Heidelberg. [Ricci et al., 2007c] Ricci, A., Viroli, M., and Omicini, A. (2007c). CArtAgO: A framework for prototyping artifact-based environments in MAS. In Weyns, D., Parunak, H. V. D., and Michel, F., editors, En- vironments for MultiAgent Systems III, volume 4389 of LNAI, pages 67–86. Springer. 3rd International Workshop (E4MAS 2006), Hakodate, Japan, 8 May 2006. Selected Revised and Invited Papers. 2