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Agent uml


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Agent uml

  1. 1. Agent UML
  2. 2. It is a changes towards Requirements Engineering
  3. 3. Introduction – traditional approach Software Engineers make changes Software System {Knowledge} Code Software Engineers Requirements Models Design Models … E.g.UMLmodels
  4. 4. Make changes Software System Auto-generate Models {Knowledge} E.g.UMLmodels • Models are reused Introduction – current thinking
  5. 5. Introduction – our thinking translate Agent Software System • Models are reused Agent-oriented Models {Knowledge} E.g.UMLmodels XMLmodels • Re-generation of the system is not necessary CASE tool Make changes
  6. 6. The Changing Needs of Requirements Modelling 1. Technology as enabler  Goals are discovered; may be bottom-up 2. Networked systems and organizations  Composite systems, but dispersed, fluid, contingent, ephemeral  Same for responsibilities, accountability, authority, ownership,… 3. Increased inter-dependency and vulnerability  Dependencies among stakeholders (inc. system elements)  Impact of changes 4. Limited knowledge and control  No single designer with full knowledge and control 5. Openness and uncertainties  Can’t anticipate all eventualities / prescribe responses in advance 6. Cooperation  Beyond vocabulary of “interaction” (behavioural)  Reason about benefits of cooperation – goals, beliefs, conflicts
  7. 7.  Requirements can be organised around agents. Agents are responsible for realising the requirements.  Agent is the container and business rule is the knowledge to fill the container in the requirements model.  Agent and business rule complements each other in the implementation: the former is the actor, the latter guides it and dictates the role that it plays.  The mapping from requirements to implementation for both the agent and the business rule brings traceability. Two additional model element – agent
  8. 8. supportsrealises is_responsible_for consists_of plays Human Agent Business Role Business Rule Business Function collaborates _with Business Agent Software Agent Business Process Business Goal Business Class contributes_to Sub-Goal relates_with participates_in Agents play business roles, their behaviour is dictated by business rules and functions Agents collaborate to realise business goals by participating in business processes Business rules and functions support business goals Meta-model Business rules and functions represent functional requirements. They become agent behaviour and class method respectively. Classes are managed by agents, and rules are configured for the invocation of methods.
  9. 9. i* - agent-oriented modelling Actors are semi-autonomous, partially knowable Strategic actors, intentional dependencies Meeting Scheduling ExampleMeeting Scheduling Example “Strategic Dependency” Model
  10. 10. Analysis and Design of Agent-Oriented Systems e.g., Wooldridge Jennings Kinny (JAAMAS 2000) “GAIA” Analysis level Roles and Interactions oPermissions oResponsibilities • liveness properties • safety properties oActivities oProtocols  Design level  Agent types  Services Modelling concepts being driven from programming again?!! • Structured Analysis from Structured Programming • OOA from OOD, OOP • AOA from AOP ??
  11. 11. What are the important concepts for Agent Orientation as a Modelling Paradigm ? Intentionality Autonomy Sociality Identity & Boundaries Strategic Reflectivity Rational Self-Interest
  12. 12. Agent Orientation as a Modelling Paradigm Intentionality Agents are intentional. Agent intentionality is externally attributed by the modeller. Agency provides localization of intentionality. Agents can relate to each other at an intentional level. Autonomy Sociality Identity & Boundaries Strategic Reflectivity Rational Self-Interest
  13. 13. Agent Orientation as a Modelling Paradigm Intentionality Autonomy An agent has its own initiative, and can act independently. Consequently, for a modeller and from the viewpoint of other agents: o its behaviour is not fully predictable. o It is not fully knowable, o nor fully controllable. The behaviour of an agent can be partially characterized, despite autonomy, using intentional concepts.  Sociality  Identity & Boundaries  Strategic Reflectivity  Rational Self-Interest
  14. 14. Agent Orientation as a Modelling Paradigm Intentionality Autonomy Sociality  An agent is characterized by its relationships with other agents, and not by its intrinsic properties alone.  Relationships among agents are complex and generally not reducible.  Conflicts among many of the relationships that an agent participates in are not easily resolvable.  Agents tend to have multi-lateral relationships, rather than one-way relationships.  Agent relationships form an unbounded network  Cooperation among agents cannot be taken for granted.  Autonomy is tempered by sociality.  Identity & Boundaries  Strategic Reflectivity
  15. 15. Agent Orientation as a Modelling Paradigm Intentionality Autonomy Sociality Identity & Boundaries Agents can be abstract, or physical. The boundaries, and thus the identity, of an agent are contingent and changeable. Agent, both physical and abstract, may be created and terminated. Agent behaviour may be classified, and generalized. Strategic Reflectivity Rational Self-Interest
  16. 16. Agent Orientation as a Modelling ParadigmAutonomy Intentionality Sociality Identity & Boundaries Strategic Reflectivity Agents can reflect upon their own operations. Development world deliberations and decisions are usually strategic with respect to the operational world. The scope of reflectivity is contingent. Rational Self-Interest
  17. 17. Agent Orientation as a Modelling ParadigmIntentionality Autonomy Sociality Identity & Boundaries Strategic Reflectivity Rational Self-Interest An agent strives to meet its goals. Self-interest is in a context of social relations. Rationality is bounded and partial.
  18. 18. What is an agent An agent is an hardware or software system placed in an environment that enjoys the following properties: oAutonomy oSocial ability oReactivity oProactivity [Wooldridge et al, 1995]
  19. 19. A stronger notion of agent Another stronger notion assigns to the agent also the following properties:  Mentalistic notions: o Beliefs o Desires o Intentions  Emotional notions: o Trust o Friendship o Suspiciousness [Wooldridge et al, 1995]
  20. 20. When agent notion is useful  The agent notion is adaptable to many HW and SW systems but it is particularly useful in such contexts where complexity is high enough to make the simple notion of object insufficient to describe the system.  The focus is on the behaviour of agents and not on the content of objects (attributes and methods).  Agent-oriented programming is at an abstraction level higher than object-oriented programming.
  21. 21. Agent-oriented programming (1)  Agent-oriented programming (AOP) is a specialisation of object-oriented programming (OOP) in the way the computational system is conceived: “The computational system is seen as composed of communicating modules, each with its own way of handling messages.” [Shoham, 1993]  The (mental) state of modules (agents) consists of components such as beliefs, capabilities and intentions.
  22. 22. Agent-oriented programming (2)  A computation consists of agents that:  Inform other agents about facts  Offer and request services  Accept or refuse proposals  Compete for accessing shared resources  Collaborate for achieving common goals OOP AOP Basic unit Object Agent Parameters defining state of basic unit Unconstrained Belief, commitments, chioces, … Process of computation Message passing and response methods Message passing and response methods Types of messages Unconstrained Inform, request, offer, promise, decline Constraints on methods None Honesty, consistency, …
  23. 23. What is Agent UML  Agent UML is a support notation for agent-oriented systems development.  It consists in using the UML modeling language and extending it in order to represent agents, their behaviour and interactions among them.  AUML is not restricted to using UML. Other approaches should be used wherever it makes sense.
  24. 24. Who is interested in AUML  OMG Special Interest Group:OMG Special Interest Group: recommends standards for agent technology where appropriate (  FIPA (Foundation for intelligent physicalFIPA (Foundation for intelligent physical Agents)Modeling Technical Commitee:Agents)Modeling Technical Commitee: tasked with developing an AUML standard (  Other methodologies:Other methodologies: MESSAGE, Gaia, Tropos, Prometheus, MaSE, ...
  25. 25. Common features of agents Agents share some common characteristics:  Identifier • identifies each agent in a multiagent system  Role • defines the behaviour of an agent into the society (es. Seller, Buyer)  Organization • defines the relationships between the roles (similar to human or animal organizations such as hierarchies, markets, groups of interest or herds)  Capability • specifies what an agent is able to do and under what conditions  Service • describes an activity that an agent can perform and is provided to other agents
  26. 26. Representation of agents  UML Class Diagrams can be used to represent the static view of agents. <<agent>> agent-name Role role 1, role 2, …, role n role dynamic 1, role dynamic 2, …, role dynamic n Organization organization 1, organization 2, …, organization n org dynamic 1, org dynamic 2, …, org dynamic n
  27. 27. Capabilities representation (1)  A capability is composed of the following parts:  Input o What the agent must receive in input to achieve his task  Output o What the capability generates as a result of the work  Input constraints o Constraints that are expected to hold in the situation before the action specified by the capability can be performed  Output constraints o Constraints hat are expected to hold in the situation after the action specified by the capability has been performed  Input-output constraints o Constraints that must hold across input and output situations  Description o A description in natural language of the capability
  28. 28. Capabilities representation (2) <<capability>> addition Input x,y:Integer Output s:Integer Description This capability makes the sum of two integers and returns an integer <<agent>> sum Role addition, subtraction rd 1 Organization calculator Protocol enter-society, exit-society compute <<capability>> subtraction Input x,y:Integer Output d:Integer Input Constraint x>=0, y>=0 Input-Output Constraint x-y >=0 Description This capability makes the difference of two integers and returns an integer • Example of capability representation: The agent ‘sum’ has two capabilities expressing the fact that he is able to make additions and subtractions They can be defined using OCL or simple logic expressions
  29. 29. Service representation (1)  A service is composed of the following parts:  Name • The name of the service  Description • A description in natural language of the service  Type • The type of the service  Protocol • A list of interaction protocols supported by the service  Agent communication language • The communication languages used in this service  Ontology • A list of ontologies supported by the service  Content language • A list of content languages supported by the service  Properties • A list of properties that discriminate the service
  30. 30. Service representation (2) <<agent>> sum Role addition, subtraction rd 1 Organization calculator Protocol enter-society, exit-society compute <<service>> computation Description This service makes an addition when requested by the request addition protocol and makes a subtraction when requested by the request- subtraction protocol Type computation Protocol request-addition request-subtraction Agent Communication Language FIPA ACL Ontology computation ontology Content Language FIPA SL • Example of service representation: The agent ‘sum’ exports a service that makes additions and subtractions on demand
  31. 31. Representing interactions  Agent interactions can be represented in UML standard using sequence diagrams
  32. 32. Concurrent interactions (1)  UML has been extended in order to represent concurrent communication acts sent from the sender agent to the receiver.  a) Concurrent communication acts from CA-1 to CA-n are sent in parallel.  b) A selection of the n acts is sent in parallel (zero or more).  c) Exclusive choice: only one of the communication acts is sent.
  33. 33. Concurrent interactions (2)  a) An agent sends 3 concurrent CA to another agent. The diagram can be interpreted in two different ways:  Every CA is processed from the same agent/role by a different thread of execution  Every CA is processed by a different role of the agent (in this case mesages can be annotated specifying the role)  b) The same semantic of (a) but with a simpler notation  c) Choice from three different communication act received by three different threads (or roles)  NOTE: each concurrent CA can be sent to different agents
  34. 34. Example of interaction  The Buyer sends a request-for- proposal to the Seller  The Seller has three options to choose within the deadline:  make a proposal  refuse (with different reasons)  say he did not understand  If the Seller has made a proposal, the Buyer has the choice to reject or to accept it  If the last is the case, the Seller schedules the proposal informing the Buyer about its the state  The Buyer can cancel the proposal execution at any time
  35. 35. Detailing interaction messages  Any interaction process can be expressed in more detail.  The “leveling” can continue down until the problem has been specified adequately to generate code.  Also activity diagrams and statecharts can be used.
  36. 36. Roles management  UML sequence diagrams can be used to represent changes in agents’ role.
  37. 37. Object role in AOP  Objects may always be included in an agent-oriented system and can communicate with agents using message passing methods.
  38. 38. References (1)  Agents  [Wooldridge et al, 1995] Wooldridge and Jennings Intelligent Agents: Theory and Practice Knowledge Engineering Review Volume 10 No 2, June 1995 Cambridge University Press (  [Shoham, 1993] Y. Shoham Agent-oriented programming Artificial Intelligence 60(1):51-92 (
  39. 39. References (2)  Agent UML   