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AOSE

AOSE. Reflections. What have we seen?. Introduced the concept of an agent Explored the notions of Interaction and Coordination GAIA & Agent Oriented Software Engineering Practical experience with Agent-Oriented Programming using Agent Factory ACRE = AOP + conversation management support.

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AOSE

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  1. AOSE Reflections

  2. What have we seen? • Introduced the concept of an agent • Explored the notions of Interaction and Coordination • GAIA & Agent Oriented Software Engineering • Practical experience with Agent-Oriented Programming using Agent Factory • ACRE = AOP + conversation management support

  3. The Larger Context • Origins of the Area • Example Toolkits • Beyond AOP…

  4. ORIGINS

  5. Distributed Artificial Intelligence • Distributed AI: “the development of distributed solutions for complex problems regarded as requiring intelligence.” • Proposed in the 1970’s. • Evolved to cover 3 main areas: • Parallel Problem Solving: Parallelisation of existing AI algorithms • Distributed Problem Solving: Development of algorithms / techniques to support decomposition and allocation of tasks + synthesis if results (coordination). • Multi-Agent Systems: DPS + reasoning about the process of coordination (run-time selection and adaptation of DPS techniques).

  6. Multi-Agent Systems • MAS: “understanding how groups of computational entities, known as agents, can collaborate and cooperate in order to solve problems that are beyond their individual capabilities.” • Emerged in the 1980’s • MAS aims to understand how human interaction and human social structures can be used to build intelligent distributed systems. • MAS research draws on a variety of fields: • Philosophy, Social Sciences, Economics / Game Theory, Linguistics, Computer Science/Engineering, …

  7. Multi-Agent Systems • At the heart of any Multi-Agent System is a core computational entity, known as an agent. • Each agent in the system is endowed with a possibly unique set of capabilities. • All agents are able to interact with one another via some form of communication infrastructure. • When an agent is assigned a task that it is incapable of completing alone, it tries to get help by attempting to locate other agent(s) that have the capabilities necessary for the task. • Getting help may require the use of negotiation strategies in order to get the best deal (payoff) for the agent. • Performing the task may require coordination of activities e.g. two robots lifting a box…

  8. Multi-Agent Systems • Generally speaking, Multi-Agent Systems are viewed as having the following characteristics: • each agent has incomplete capabilities to solve a problem • there is no global system control • data is decentralized • computation is asynchronous

  9. So, what is an Agent? • The term “agent” means different things to different people. “An agent is a computer system that is situated in some environment, and that is capable of flexible, autonomous action in this environment in order to meet its design objectives” (Wooldridge and Jennings, 1995)

  10. So, what is an Agent? • According to (Russell and Norvig, 1995), an agent is: • “anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators” • Thus, they view an agent as: • any entity that is located in some environment, and which • interacts with that environment through a set of sensors and actuators. • They then extend this definition to identify an intelligent agent as any agent that embodies some AI technique.

  11. So, what is an Agent? • In contrast, (Maes, 1995) views agents to be: • “computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realise a set of goals or tasks for which they are designed.” • This posits a view of an agent as: • any autonomous software entity that is located in a complex dynamic environment, and which • exhibits goal-oriented behaviour, requiring that it act in pursuit of its own goals.

  12. So, what is an Agent? • Alternatively, (Shoham, 1993) adopts the perspective that: • “An agent is an entity whose state is viewed as consisting of mental components such as beliefs, capabilities, choices, and commitments. These components are defined in a precise fashion, and stand in rough correspondence to their common sense counterparts” • This third definition adopts the view of agents as mental entities: • That is, entities that employ mental concepts such as beliefs, commitments, and goals in order to reason about both the environment and their activities…

  13. Weak and Strong Agency • In 1995, Michael Wooldridge and Nick Jennings proposed a two-tier definition of agency that has become a de facto standard for agent research. • The lower tier, or weak notion of agency, was intended to be sufficiently general to meet the needs of most agent researchers, and specified the following agent attributes: • Autonomy, social ability, reactivity, and pro-activity. • The upper tier, or stronger notions of agency, were intended to build on this weak core to provide more specific definitions, and specified attributes such as: • Benevolence, rationality, mobility, learning, intentionality, …

  14. Weak Agency • Autonomy: Agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state. • Social Ability: Agents interact with other agents and (possibly) humans via some kind of agent communication language. • Reactivity: Agents perceive their environment (which may be the physical world, a user via a graphical user interface, a collection of other agents, the Internet, or perhaps all of these combined), and respond in a timely fashion to changes that occur in it. • Pro-activity: Agents do not simply act in response to their environment, they are able to exhibit goal-directed behaviour by taking the initiative

  15. Stronger Agency • Mobility: the ability of an agent to move around an electronic network. • Benevolence: Is the assumption that agents do not have conflicting goals, and that every agent will therefore always try to do what is asked of it. • Rationality: is (crudely) the assumption that an agent will act in order to achieve its goals and will not act in such a way as to prevent its goals being achieved - at least insofar as its beliefs permit. • Intentionality: an agent reasons about its activities through the application of mental notions such as beliefs, goals, obligations, commitments, intentions…

  16. Agents versus Objects • Objects are passive (cannot take the initiative). • Agents are (pro-)active objects • Objects do not encapsulate behaviour activation (not able to say no) • Objects only encapsulate state and behaviour • Objects are insufficient for modeling complex systems • From (Booch, 1994) as an argument for Design Patterns and Application Frameworks

  17. Multi-Agent Research Themes • Agent-Oriented Software Engineering • Agent Programming Languages & Architectures • Methodologies / Model Driven Development • Tool Support: Debugging / Profiling • Metrics / Evaluation of MAS • Interoperability / Scalability / Adaptability / … • Cooperation, Coordination & Negotiation • Agent Communication Languages (KQML, FIPA) • Coordination Languages & Models (LINDA, CLAIM) • Market-based coordination (Game Theory) • Distributed Constraint Satisfaction Problems • Organisations & Environments

  18. Types of Agent • Deliberative Agents • What we have seen in this course (knowledge-based systems) • Focus on individual agent and its decision-making process • Reactive Agents • Use the world as its own model (remove the knowledge) • Maximise responsiveness to the environment (real-time) • Social Agents • Focus on conversation management / interaction • Explore self interest, trust, aquaintance modelling, …

  19. Types of Agent • Deliberative Agents • What we have seen in this course (knowledge-based systems) • Focus on individual agent and its decision-making process • Reactive Agents • Use the world as its own model (remove the knowledge) • Maximise responsiveness to the environment (real-time) • Social Agents • Focus on conversation management / interaction • Explore self interest, trust, aquaintance modelling, … GOAL-BASED REASONING REACTIVITY SOCIAL ABILITY

  20. Types of Agent • Deliberative Agents • What we have seen in this course (knowledge-based systems) • Focus on individual agent and its decision-making process • Reactive Agents • Use the world as its own model (remove the knowledge) • Maximise responsiveness to the environment (real-time) • Social Agents • Focus on conversation management / interaction • Explore self interest, trust, aquaintance modelling, … • Mobile Agents: Code migration over a network GOAL-BASED REASONING REACTIVITY SOCIAL ABILITY

  21. JaDE TOOLKIT

  22. JADE • FIPA-compliant distributed agent platform which can be split onto several hosts. • Java Application Programmer’s Interface. • Library of FIPA interaction protocols, such as Contract Net, ready to be used. • Graphical User Interface to manage several agents from the same Remote Management Agent. • Available at http://jade.cselt.it/

  23. JADE Agent Platform

  24. Defining a JADE Agent import jade.core.Agent; import jade.core.AID; public class BookBuyerAgent extends Agent { // Put agent initializations here protected void setup() { // Printout a welcome message System.out.println(“Hello! Buyer-agent “+getAID().getName()+” is ready.”); } // Put agent clean-up operations here protected void takeDown() { // Printout a dismissal message System.out.println(“Buyer-agent “+getAID().getName()+” terminating.”); } }

  25. The JADE Behaviour Model • Agents perform tasks. • Tasks are modelled as Behaviours. • Typically, an agent will perform several concurrent tasks in response to different external events. • Every JADE agent is composed of a single execution thread. • Concurrent tasks are modelled and can be implemented as instances of jade.core.behaviours.Behaviour

  26. The Behaviour class • Tasks are associated with an agent via the addBehaviour() method of the Agent class. • Each Behaviour subclass must implement • public void action(): what the behaviour actually does • public boolean done(): Whether the behaviour is finished • Parallel behaviours are cooperative (not preemptive) and occur within a single Java Thread • Behaviour switch occurs only when the action() method of the currently scheduled behaviour returns.

  27. Behaviour types • Basic Behaviours: • “One shot” behaviours • “Cyclic” behaviours • Support for More “Complex” (composite) behaviours…. • WakerBehaviour • Implement the onWake() method which is executed after a given timeout. • After that execution the behaviour completes. • TickerBehaviour • Implement the onTick() method which is executed periodically with a given period • The behaviour runs forever unless its stop() method is executed

  28. JADE Agent Control Algorithm

  29. JADE Tool Support • Management, control, monitoring, and debugging of a multi-agent platform • RMA (Remote Monitoring Agent) • Dummy Agent • Sniffer Agent • Introspector Agent • Log Manager Agent • DF (Directory Facilitator) GUI

  30. Mobility • “Hard mobility” • Status: an agent can • stop its execution on the local container • move to a remote container (likely on a different host) • restart its execution there from the exact point where it was interrupted. • Code: If the code of the moving agent is not available on the destination container it is automatically retrieved on demand. • An agent must be Serializable in order to move • Mobility can be • self-initiated through doMove() of the Agent class • forced by the AMS (following a request from another agent)

  31. Advanced features • Using JADE (i.e. creating a container and starting agents) from an external Java program (servlets, applets, JSP..) • Executing a normal JADE behavior in a dedicated thread • Saving and reloading agent state on relational DB (persistence based on www.hibernate.org); add-on • Integration with JESS (Java Expert System Shell) • it allows reasoning about messages in JESS • it allows a JESS program to control sending/receiving messages and/or creating/destroying JADE behaviours

  32. Advanced features • Distributed security, fault tolerance, support for replicated agents and services • Protégé, XML, RDF and OWL • Application-specific persistent delivery filters & JADE kernel-level services • The LEAP add-on (j2ME-compliant version of JADE)

  33. BEYOND AOP…

  34. Abstractions in MAS

  35. Abstractions in MAS • Individual Agent Level: autonomy, situatedness • Cognitive Concepts: beliefs, desires, goals, intentions, plans • Reasoning Cycle: sense/reason/act, reactive/pro-active behaviour • Environment Level: resources and services that agents can access and control; sensing and acting in an environment • Social and Organisation Level: cooperation, coordination, regulation patterns • Roles: rights, responsibilities, ... • Organisational Rules: constraints on roles and their interactions, norms, deadlines, ... • Organisational Structures: topology of interaction patterns and relations over activity control

  36. Programming Agents • Programming Languages for Multi-Agent Systems • E.g., Jason, Jadex, JACK, 2APL, GOAL, Brahms, JIAC, Agent Factory, MetateM, Golog variants, ... • Architecture to represent an agent mental state: • Beliefs: information available to agent (e.g., about the environment or other agents) • Goals: states of aairs that the agent wants to achieve • Events: changes in agents beliefs or goals • Capabilities: reusable modules of activities that the agent can perform • Plans: reasoning about courses of action to achieve goals • Rules: reasoning about beliefs

  37. Programming Agents • Some steps of a Reasoning Cycle: • Determining Relevant Plans for Handling Events • Select a Plan for Execution • Execute Part of an Intended Plans • Handle Plan Failures • Agent Interpreter is an infinite loop of such reasoning cycles. • The architecture and reasoning cycle together with the agent program (specially plans) determine the behaviour of the agent.

  38. Programming Organisations • Concepts used to specify the state of an organisation: • Agents, Roles, Groups • Norms, Obligations, Prohibitions, Permissions, Violations • Dependency, Power, Delegation, Information ow relations • Deadlines, Sanctions, Rewards • Management Infrastructure to control and coordinate agent behaviour at run-time: • Endogenous: The control is a part of the agent program • Exogenous: The control is performed by an external system • Monitoring Agent Behaviour • Enforcing Organisational Rules • Regimenting Organisational Rules

  39. Programming Environments • Artifacts to represent the state of the environment • Access to Databases/Services/etc., Coordination, Interaction • Environment "objects", i.e., non-proactive entities • Processing Operations on Artifacts • Realising the effects of environments actions • Providing events related to sensing the environment • Synchronising agent actions • At the right level of abstraction for a multi-agent system

  40. WHY ENVIRONMENTS?

  41. Agents and Environments • The notion of environment is intrinsically related to the notion of agent and multi-agent system • “An agent is a computer system that is situated in some environment and that is capable of autonomous action in this environment in order to meet its design objective" [Wooldridge, 2002] • “An agent is anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors." [Russell and Norvig, 2003] • Including both physical and software environments

  42. Environment Programming: Issues • Defining the interface • actions, perceptions • data-model • Defining the environment computational model & architecture • how the environment works • structure, behaviour, topology • core aspects to face: concurrency, distribution • Defining the environment programming model • how to program the environment

  43. Basic Level Overview

  44. Single Agent Perspective • Perception • process inside agent inside of attaining awareness or understanding sensory information, creating percepts perceived form of external stimuli or their absence • Actions • the means to affect, change or inspect the environment

  45. Multi-Agent Perspective • In evidence • overlapping spheres of visibility and inuence • ..which means: interaction

  46. 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 • endogenous environments (vs. exogenous ones) • programmable environments

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