4th Summer School AACIMP-2009
             Achievements and Applications of
        Contemporary Informatics, Mathematics ...
Multi Agent System (MAS)
“A modeling and computational approach
    considering that simple or complex
 activities can be ...
How to describe a phenomenon (solve
a problem) as the result of collective
              behavior
• Modeling the problem a...
Design of a MAS
     What should be modeled?
•   Agents
•   Organization
•   Interactions
•   Environment
Design of a MAS (1)
               Agents
• Agent architecture (Internal structure)
  and agent behavior (Agent model)
  –...
Design of a MAS
                  (2) Organization

Fixed,              Variable according to   Variable,
predefined      ...
Design of a MAS (3)
         Interactions (1/3)
• An interaction occur when two or more agents
  are brought into a dynami...
Interactions assume ...
• Presence of agents capable of interacting
  and/or communicating
• Situations which can serve as...
Interactions and organizations
• Interactions are an element necessary for
  the setting up of social organization
• Group...
Interaction situation
  An assembly of behaviors resulting from the grouping of
      agents which have to act in order to...
Example – Building of a house
• Type of interaction  Cooperation situation
  requiring coordination of actions

• Interac...
Collective robotics
Bio-inspired opt algorithms
A classification of
          Interaction situations
• According to compatibility of goals
   – Agents cooperate when thei...
Compatibility of goals in reactive
             agents
• Negative interaction: the survival
  behavior of the one entail t...
Symbiosis and prey-predator
• Symbiosis between organisms A and B (e.g. A
  nourishes B and B defends A from predators):
 ...
Resources
                                                 Co
                                                ac nfli
    ...
Solving conflict situations
          with coordination
• Synchronization (from distributed
  systems research)
   – of mo...
Coordination in forest ecosystem
                                 Different plant species can inhabit
                    ...
Interaction through resources

• The presence of a plant limits the
  sunlight diffusion to neighbours and
  seeds’ growth...
Agents skills and tasks

• Tasks
   – can be carried out by a single alone (no interaction
     required)
   – can be carr...
Types of interaction (1)

     Goals               Resources           Skills            Type

     Compatible          Su...
Types of interaction (2)
• Independence (G, R, S): simple juxtaposition of actions
  carried out by agent independently wi...
Types of interaction (3)
• Pure individual competition (g, R, S): resources are not
  limited and the competition is not r...
INTERACTION MODELS IN
          MULTI-AGENT SYSTEMS
• Agent internal architecture can be separated by the
  (interaction) ...
INTERACTION MODELS IN MAS:
             A TAXONOMY
                                 With a-priori
                        ...
Direct interaction models
• Agents are able to directly exchange
  information
• Information exchange
  – Communication/co...
Direct interaction model example: KQML
• Knowledge Query and Manipulation Language (KQML) and
  Knowledge Interchange Form...
KQML Message (speech act)

                   (tell :sender      bookShopAgent123
performative             :receiver     k...
A KQML Dialogue
Agents A and B “talking” about the prices of
books bk1 and bk2:

A to B: (ask-if (> (price bk1) (price bk2...
KQML performatives
Some requirements
• Agents need to know their communication partners
  – Common approach is to have specific facilitators ...
Other tools for communication
              semantics

• The specification of conversations can be
  done through several ...
Direct interaction models: pros
• Similarity to existing protocols for distributed
  systems
   – Point-to-point message p...
Direct interaction models: cons
• Information exchange occurs according to specific rules
   – Network protocol like issue...
Direct interaction models:
      some enhancements
• Discovery issue and agent context
  – Middle agents as specific agent...
INTERACTION MODELS IN MAS:
             A TAXONOMY
                                 With a-priori
                        ...
Indirect interaction models

• Agents interact through an intermediate entity

• This medium supplies specific interaction...
INTERACTION MODELS IN MAS:
             A TAXONOMY
                                 With a-priori
                        ...
Artifact-mediated interaction

• Agents access a shared artifact that
   – they can observe
   – they can modify

• Such a...
Blackboard systems
 “Metaphorically we can think of a set of workers,
all looking at the same blackboard: each is able to
...
Linda: a specific blackboard based system

• Tuple space: a sort of blackboard in which tuples
  (record-like data structu...
Matching rules in Linda

• Example:
  out("string", 10.1, 24, "another string")
  real f; int i;
  rd("string", ?f, ?i, "a...
From Linda, to mobility and beyond

• Distributed tuple spaces: these systems
  allow to have a conceptually shared tuple
...
Artifact-mediated interaction
    models: pros and cons
• Advantages
  – The artifact represents an abstraction of agents’...
INTERACTION MODELS IN MAS:
             A TAXONOMY
                                 With a-priori
                        ...
Spatially founded interaction
• Artifact mediated interaction are a first step in
  agents’ environment modelling
• Such a...
Ancestors of Spatial Interaction: CAs
• A Cellular Automata (CA) is a set of homogeneous cells,
  evolving in discrete tim...
Swarm (and the likes) agent
      environment
• Swarm and many derived projects
  provide specific environments in which
 ...
A coordination model for
                  self-organizing agents
[S. Bandini, S. Manzoni, C. Simone, Dealing with Space i...
Coordination as result of interactions

Field-based interaction model

- Indirect interaction model between
agents

- Intr...
Interaction through Fields
• Fields are generated by agents to interact at-a-distance and
  asynchronously

• f = <Wf, Dif...
Agents Perception

                          Set of states that agents of type T can assume

  T  < ∑T, PerceptionT, Acti...
Field based interaction: emission & perception
                              emit(f)
  CompareT(f×c,t) = false            ...
Agent Coordination Language:
                primitives
action: emit(s,f,p)
condit: state(s)
effect: present(f, p)

action...
Subway station scenario
• Various crowd behaviors can take
  place
• Passengers' behaviors difficult to
  predict
• Crowdi...
Coordinated movement in space
Interactions in Multi Agent Systems
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Interactions in Multi Agent Systems

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AACIMP 2009 Summer School lecture by Sara Manzoni. "Mathematical Modelling of Social Systems" course. 4th hour.

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Interactions in Multi Agent Systems

  1. 1. 4th Summer School AACIMP-2009 Achievements and Applications of Contemporary Informatics, Mathematics and Physics Interactions in Multi Agent Systems Lecture 2 – 12.08.2009 Dr. Sara Manzoni Complex Systems and Artificial Intelligence research center Department of Computer Science, Systems and Communication University of Milano-Bicocca
  2. 2. Multi Agent System (MAS) “A modeling and computational approach considering that simple or complex activities can be the fruits of interaction between autonomous and independent entities (i.e. agents) which operate within communities (i.e. organized structures) in accordance with modes of cooperation (= collaboration + coordination + conflict resolution) in order to fulfill given goals”
  3. 3. How to describe a phenomenon (solve a problem) as the result of collective behavior • Modeling the problem as a structured set of entities (i.e. organization) able to – Act in an environment – Interact: communicate and cooperate in order to fulfill (common) tasks – Perceive (locally) the environment and adapt their behavior according to perceptions – Possess their own resources, skills, tendencies and objectives (explicit or implicit) – Behave (e.g. plan actions) tending towards the satisfaction of objectives, taking into account available resources, according to their skills, and depending on their perceptions
  4. 4. Design of a MAS What should be modeled? • Agents • Organization • Interactions • Environment
  5. 5. Design of a MAS (1) Agents • Agent architecture (Internal structure) and agent behavior (Agent model) – actions that can be undertaken – environment perception – adaptation mechanism – goal fulfillment mechanism • Tools: operative modeling, formalization and specification languages, knowledge representation languages – E.g. production rules, Petri nets
  6. 6. Design of a MAS (2) Organization Fixed, Variable according to Variable, predefined predefined structure structure (e.g. mechanisms (e.g. emerging from Hierarchy) auction protocols) system behaviour Leaving aside the dynamic dimension, an organization can be defined and analyzed – Functionally (roles, tasks, capacities) – Structurally (divisions, interconnections, relationships)
  7. 7. Design of a MAS (3) Interactions (1/3) • An interaction occur when two or more agents are brought into a dynamic relationship through a set of reciprocal actions • Interactions develop out of a series of actions whose consequences in turn have an influence on the future behavior of agents • During interactions, agents are in contact with each other – Directly – Through another agent – Through the environment
  8. 8. Interactions assume ... • Presence of agents capable of interacting and/or communicating • Situations which can serve as meeting point of agents • Dynamic elements allowing local and temporary relationships between agents • “slack” in relationships between agents enabling them to detach themselves from it (agent autonomy)
  9. 9. Interactions and organizations • Interactions are an element necessary for the setting up of social organization • Groups are – the result of interactions – the preferred locations in which interactions occur • Interaction is the crucial element in organizations  Source and Product of the permanence of the organization
  10. 10. Interaction situation An assembly of behaviors resulting from the grouping of agents which have to act in order to attain their objectives, with attention being paid to the more or less resources which are available to them and their individual skills • A concept introduced to describe activities of agents in order to identify different types of interactions by linking interactions to the elements of which they are composed • Defines abstract interaction categories independent of their concrete realizations, by distinguishing them according to – Main invariables that we find everywhere – Differences between situations
  11. 11. Example – Building of a house • Type of interaction  Cooperation situation requiring coordination of actions • Interaction situation in which the assembly of behaviors of the agents (i.e. workforce, architect, owner, project manager, ...) is characterized by their own objectives (the same house looked at from the viewpoints of different agents) and their skills (know-how of the architect and of different skilled workers) with attention being paid to the available resources (raw materials, financing, tooling, building site)
  12. 12. Collective robotics Bio-inspired opt algorithms
  13. 13. A classification of Interaction situations • According to compatibility of goals – Agents cooperate when their goals are compatible  positive interaction situations – Agents compete when their goals are incompatible  negative interaction situations • According to agent ability to available resources – Conflict arises when resources are insufficient  negative interaction situations • According to agent ability to fulfill tasks – Collaboration arises when agents have insufficient ability to solve complex problems  positive interaction situations
  14. 14. Compatibility of goals in reactive agents • Negative interaction: the survival behavior of the one entail the death of the other • Positive interaction: the behavior of the one is not negatively affected by that of the other – Cooperation: the behavior of the one is reinforced by the behavior of the other • Indifference: the behavior of the one is not affected at all (neither positively nor negatively) by the behavior of the other
  15. 15. Symbiosis and prey-predator • Symbiosis between organisms A and B (e.g. A nourishes B and B defends A from predators): reactive cooperation – Heterogeneous organisms cooperate since each organism is reinforced by the presence and behavior of other one • Prey-predator model: antagonistic cooperation – Predators cooperate (e.g. group formation) to hunt the prey – Antagonistic relationship between predators and their preys – the survival of preys entails the failure of predators
  16. 16. Resources Co ac nfli ce ct ss zo • All the environmental When? in ne gr and material es for ou elements that can be Resources rc e s used by agents to wanted by A carry out their actions • Conflicts arise when two or more agents Resources need the same wanted by B resources at the same time and in the same Where? place
  17. 17. Solving conflict situations with coordination • Synchronization (from distributed systems research) – of movements – of access to resources • Coordination by planning (from AI): Multi-agent planning – Centralized planning for multiple agents – one planner – Centralized coordination for partial planning – one coordinator – Distributed planning • Reactive coordination – Coordination by situated actions (potential fields or marking the environment) • Coordination by regulation: rules – to anticipate and eliminate a- priori conflict situations – to manage conflict resolution
  18. 18. Coordination in forest ecosystem Different plant species can inhabit the same area and compete for the same resources Competition on available Each portion of the territory - can be inhabited by a tree resources, needed for - contains a given amount of resources survival and reproduction needed by plants to sprout, grow, survive, and reproduce themselves C = {R, P, M, T, S}, where: R = {R1,…,Rm} – amount of resources M = {M1,…,Mm} – maximum amount of each resource P = {P1,…, Pm} – amount of each resource produced by the cell at each update step T – plant state (if any) S = {s1,...,sn} – number of seeds of each species present in the cell
  19. 19. Interaction through resources • The presence of a plant limits the sunlight diffusion to neighbours and seeds’ growth • Different species have different needs in terms of resources • Resources are produced and consumed by plants • Resource distribution on the territory
  20. 20. Agents skills and tasks • Tasks – can be carried out by a single alone (no interaction required) – can be carried out alone but the accomplishment is facilitated by the support of other agents – need several agents to be accomplished • In cases of interaction, the resulting system posses new properties that can be described as new emerging functionalities – the produced object is more than the simple sum of the skills of each of the agents – interactions between agents enhance the result
  21. 21. Types of interaction (1) Goals Resources Skills Type Compatible Sufficient Sufficient Independence Compatible Insufficient Sufficient Obtrusion Compatible Insufficient Insufficient Coordinate Collaboration Incompatible Sufficient Sufficient Individual Competition Incompatible Sufficient Insufficient Collective Competition Incompatible Insufficient Sufficient Individual Conflict on resources Incompatible Insufficient Insufficient Collective Conflict on resources J. Ferber, “Multi-Agent Systems: an introduction to distributed artificial intelligence”, 1999
  22. 22. Types of interaction (2) • Independence (G, R, S): simple juxtaposition of actions carried out by agent independently without effective interaction • Simple collaboration (G, R, s): simple addition of skills, without requiring coordination of actions (e.g. When knowledge is shared among agents) • Obstruction (G, r, S): agents get in touch in accomplishing their tasks, but they do not need one another • Coordinated collaboration (G, r, s): agents have to coordinate their actions to have synergic advantages of pooled skills (e.g. industrial activities, network control, design and manufacturing of product) – most complex coordination
  23. 23. Types of interaction (3) • Pure individual competition (g, R, S): resources are not limited and the competition is not related to them (e.g. running racing) • Pure collective competition (g, R, s): agents have to group into coalitions or associations to be able to achieve their goals. Two phase process: individuals ally into groups + groups are set one against another (e.g. sailing competition) • Individual conflict over resources (g, r, S): the object of conflict is the insufficient resource (e.g. Territory, financial position, animals defending their territory, humans willing to obtain a better job) • Collective conflicts over resources (g, r, s): all forms of collective conflicts in which the objective is to obtain possession of territory or a resource (e.g. Wars, monopoly of a good) – collective competition + individual conflict on resources
  24. 24. INTERACTION MODELS IN MULTI-AGENT SYSTEMS • Agent internal architecture can be separated by the (interaction) model that defines the way agents communicate • This approach allows the modelling, design and implementation of heterogeneous entities, sharing an environment in which they can interact • Many different interaction models have been defined and implemented • Often inspired by other disciplines (e.g., social science, linguistics, biology)
  25. 25. INTERACTION MODELS IN MAS: A TAXONOMY With a-priori acquaintance Direct Agent discovery interaction through middle agents Middle agents & Agent acquaintance models interaction Guided/mediated by artifacts Indirect interaction Spatially founded interaction
  26. 26. Direct interaction models • Agents are able to directly exchange information • Information exchange – Communication/conversation rules (“protocol”)  Agent Communication Language (ACL) – Message structure (shared ontology)  Content Language • Information exchange is indiscriminate – Once an agent knows another one, it will be able to communicate with it – No external, contextual factors are considered
  27. 27. Direct interaction model example: KQML • Knowledge Query and Manipulation Language (KQML) and Knowledge Interchange Format (KIF) are results of the ARPA Knowledge Sharing Effort – KQML is an ACL, a high level interaction language – KIF is a content language, defining syntax of contents • KQML defines performatives (basic messages to compose conversations among agents) • KIF allows to represent information and knowledge about agents, beliefs, desires, intentions, perceptions plans and thus their environment • Agents must share an ontology, in terms a common vocabulary and agreed upon meanings to describe a domain subject
  28. 28. KQML Message (speech act) (tell :sender bookShopAgent123 performative :receiver ksAgent :in-reply-to id7.34.96.45391 parameter :ontology books value :language Prolog :content “price(ISBN3429459,24.95)”) A KQML speech act is described by a list of attribute/value pairs e.g. :content, :language, :from, :in-reply-to.
  29. 29. A KQML Dialogue Agents A and B “talking” about the prices of books bk1 and bk2: A to B: (ask-if (> (price bk1) (price bk2))) B to A: (reply true) B to A: (inform (= (price bk1) 25.50)) B to A: (inform (= (price bk2) 19.99)) For convenience message format above is simplified and attribute/value pairs for :ontology etc. are omitted.
  30. 30. KQML performatives
  31. 31. Some requirements • Agents need to know their communication partners – Common approach is to have specific facilitators that are known by every agent and allow them to get acquainted – Problems: how many of those ‘middle agents’ (robustness) ? How to keep the aligned ? • A semantic must be defined to obtain/enforce meaningful conversations – Agent considered as a logical reasoner with beliefs, desires and intentions – Pre and post conditions defined in terms of a of logic formalization – Actualization of postconditions triggers preconditions of other performatives – What about autonomy ?
  32. 32. Other tools for communication semantics • The specification of conversations can be done through several formal models – Finite State Machines based – Petri nets based • The former approach has been widely used to model, analyze and demonstrate properties of network protocols • These appraches also limit agents’ autonomy
  33. 33. Direct interaction models: pros • Similarity to existing protocols for distributed systems – Point-to-point message passing – Easy implementation on top of existing middleware platforms • Simple integration with deliberative agents approach – Agents exchange facts conforming to some kind of formally defined ontology • Formal semantics of ACLs can be easily specified – Communication semantics is related to agents’ beliefs, decisions, intentions
  34. 34. Direct interaction models: cons • Information exchange occurs according to specific rules – Network protocol like issues (conversation rules, message formats)  Semantical issues • communication semantics related to agent internals (beliefs, decisions, intentions) • normative semantics limits agents’ autonomy • Exchanged information must conform to an ontology that is somehow shared by the agents  Ontology issue • Agents need to be aware of the presence of a communication partner  Discovery issue • Direct interaction models do not provide abstractions to represent elements of agents context
  35. 35. Direct interaction models: some enhancements • Discovery issue and agent context – Middle agents as specific agents collecting and providing acquaintance information to entities of the system – Not a single middle agent, but a network of them, organized in order to provide robustness and structure – Not just mere agent name service, but information on provided services
  36. 36. INTERACTION MODELS IN MAS: A TAXONOMY With a-priori acquaintance Direct Agent discovery interaction through middle agents Middle agents & Agent acquaintance models interaction Guided/mediated by artifacts Indirect interaction Spatially founded interaction
  37. 37. Indirect interaction models • Agents interact through an intermediate entity • This medium supplies specific interaction mechanisms and access rules • These rules and mechanisms define agent local context and perception • Time and space uncoupling • Name uncoupling
  38. 38. INTERACTION MODELS IN MAS: A TAXONOMY With a-priori acquaintance Direct Agent discovery interaction through middle agents Middle agents & Agent acquaintance models interaction Guided/mediated by artifacts Indirect interaction Spatially founded interaction
  39. 39. Artifact-mediated interaction • Agents access a shared artifact that – they can observe – they can modify • Such artifact is a communication channel characterized by an intrinsically broadcast transmission • Specific laws regulating access to this medium • It represents a part of agents’ environment
  40. 40. Blackboard systems “Metaphorically we can think of a set of workers, all looking at the same blackboard: each is able to read everything that is on it, and to judge when he has something worthwhile to add to it.” (A. Newell, 1962) W1 W2 Wn Concurrent access control Blackboard
  41. 41. Linda: a specific blackboard based system • Tuple space: a sort of blackboard in which tuples (record-like data structures) can be inserted, inspected and extracted by agents • Operations – out(t) puts a new tuple in the Tuple Space, after evaluating all fields; the caller agent continues immediately – in(t) looks for a tuple in the Tuple Space; if not found the agent suspends; when found, reads and deletes it – rd(t) looks for a tuple in the Tuple Space; if not found the agent suspends; when found, reads it – inp(t) looks for a tuple in the Tuple Space; if found, deletes it and returns TRUE; if not found, returns FALSE – rdp(t) looks for a tuple in the Tuple Space; if found, copies it and returns TRUE; if not found, returns FALSE
  42. 42. Matching rules in Linda • Example: out("string", 10.1, 24, "another string") real f; int i; rd("string", ?f, ?i, "another string")  succeeds in("string", ?f, ?i, "another string")  succeeds rd("string", ?f, ?i, "another string")  does NOT succeed • Example: out(1,2) rd(?i,?i)  does not succeed (whatever is the type of i)
  43. 43. From Linda, to mobility and beyond • Distributed tuple spaces: these systems allow to have a conceptually shared tuple space that is spread in a distributed environment • More than just distribution – Programmable, reactive tuple spaces: adding a behaviour to tuple spaces – Including organizational abstractions (roles, policies) to enhance access rules • References: M. Mamei, F. Zambonelli
  44. 44. Artifact-mediated interaction models: pros and cons • Advantages – The artifact represents an abstraction of agents’ environment, and the burden of interaction is moved from the agents to their environment – Interaction is mediated, and can thus be controlled (enforcement/enactment of organizational rules) • Issues – Complex implementation (in distributed environments) – How to integrate different artifacts and contexts ?
  45. 45. INTERACTION MODELS IN MAS: A TAXONOMY With a-priori acquaintance Direct Agent discovery interaction through middle agents Middle agents & Agent acquaintance models interaction Guided/mediated by artifacts Indirect interaction Spatially founded interaction
  46. 46. Spatially founded interaction • Artifact mediated interaction are a first step in agents’ environment modelling • Such artifacts represent very focused parts of the environment, and cannot consider the parts of agents’ context that does not pertain the specific artifact – They represent a single specific context of interaction • Other approaches bring the environment metaphor to a deeper level, providing spatially founded interaction mechanisms • Spatial features of the environment are explicitily considered by interaction mechanisms
  47. 47. Ancestors of Spatial Interaction: CAs • A Cellular Automata (CA) is a set of homogeneous cells, evolving in discrete time steps • Cells form a regular n-dimensional lattice – Homogeneous neighborhood (e.g. Von Neumann, Moore) • Cells characterized by – A state, belonging to a finite set representing possible cell states – A transition rule, describing cell state dynamics • Cell  sort of reactive agent – Which cannot move in the environment – Can only interact with neighbouring cells according to precisely defined rules von Neumann Moore Extended Neighbourhood Neighbourhood Moore Neighbourhood
  48. 48. Swarm (and the likes) agent environment • Swarm and many derived projects provide specific environments in which agents may be placed and interact • Regular lattices supporting diffusion of signals that are – Emitted by entities – Spread in the spatial structure – Affecting other entities – Evaporating over time • Diffusion strictly related to specific environmental structures
  49. 49. A coordination model for self-organizing agents [S. Bandini, S. Manzoni, C. Simone, Dealing with Space in Multi-Agent System: a model for Situated MAS, in Proc. of AAMAS 2002, ACM Press, New York, 2002] Spatial structure SCA (MMASS) – Formal and computational framework where to describe, represent and simulate complex systems according to a situated Agents and MAS approach behaviours At-a-distance interaction
  50. 50. Coordination as result of interactions Field-based interaction model - Indirect interaction model between agents - Intrinsically multicast - Agent interactions occur when agent states are “compatible”
  51. 51. Interaction through Fields • Fields are generated by agents to interact at-a-distance and asynchronously • f = <Wf, Diffusionf, Comparef, Composef> – Wf: set of field values – Diffusionf: P X Wf X P Wf X…XWf field distribution function – Composef: Wf …XWf Wf field composition function – Comparef: Wf X Wf  {True, False} field comparison function
  52. 52. Agents Perception Set of states that agents of type T can assume T  < ∑T, PerceptionT, ActionT> Set of allowed actions for agents of type T PerceptionT: ∑T [N X Wf1] …[N X Wf|F|] •PerceptionT(s) = (cT(s), tT(s)) •cT(s): coefficient applied to field values •tT(s): sensibility threshold to fields •An agent perceives a field fi when CompareT(ciT(s)…wfi,tiT(s)) is True
  53. 53. Field based interaction: emission & perception emit(f) CompareT(f×c,t) = false • Fields are signals emitted by agents and diffused in the environment • Their intensity is possibly modulated in their diffusion • Other agents may perceive these signals according to their perceptive capability, state and the signal value they receive • Effect of perception CompareT(f×c,t) = true defined by agent behavioural specification CompareT(f×c,t) = false
  54. 54. Agent Coordination Language: primitives action: emit(s,f,p) condit: state(s) effect: present(f, p) action: trigger(s,fi,s’) condit: state(s), perceive(fi) effect: state(s’)
  55. 55. Subway station scenario • Various crowd behaviors can take place • Passengers' behaviors difficult to predict • Crowding dynamics emerges – Social interactions between passengers  social rules – Interactions between single passengers and the environment (signs, doors, constraints) action: transport(p,fi,q) condit: position(p), empty(q), near(p,q), perceive(fi) effect: position(q), empty(p)
  56. 56. Coordinated movement in space
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