On Experimenting with AgentSpeak(L) On Experimenting with AgentSpeak(L) AgentsAgents

Ioannis Svigkossvigkoi@westminster.ac.uk

June 2004

Harrow School of Computer Science

• Part I:Part I:– Agent Technology.Agent Technology.

• Part II:Part II:– BDI Agents.BDI Agents.– AgentSpeak(L).AgentSpeak(L).– Agentspeak(L) constructsAgentspeak(L) constructs– Agentspeak(L) InterpreterAgentspeak(L) Interpreter– Limitations.Limitations.

• Part III:Part III:– Demonstraing AgentSpeak(L)Demonstraing AgentSpeak(L)

O v e r v i e wO v e r v i e w

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Part I: Agent TechnologyPart I: Agent Technology

Software SystemsSoftware Systems

• Computing environments:Computing environments:– ApplicationsApplications– Information Information – Components are dispersedComponents are dispersed

• Networking technologiesNetworking technologies• InformationInformation

– Increases in size, many formsIncreases in size, many forms– Unstructured, altered without noticeUnstructured, altered without notice

• Future of computingFuture of computing– Autonomic, Pro-active, Ubiquitous, PervasiveAutonomic, Pro-active, Ubiquitous, Pervasive

… … a solution can be …a solution can be …On Experimenting with AgentSpeak(L) Agents.

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• An Agent is ….An Agent is ….

• DimensionsDimensions– AutonomyAutonomy– Situated-nessSituated-ness– Responsive-nessResponsive-ness– Proactive-nessProactive-ness– Social abilitySocial ability

… … a particular kind of agents …a particular kind of agents …

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Agent TechnologyAgent Technology

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Part II: BDI AgentsPart II: BDI Agents

• Decision making model based:Decision making model based:

– Human Practical reasoningHuman Practical reasoning

– Includes two processes:Includes two processes:

• DeliberationDeliberation

• Means-end ReasoningMeans-end Reasoning

• BDI:BDI:

– Belief, Desire Intention ModelBelief, Desire Intention Model

… … But why BDI agents ? But why BDI agents ?

On Experimenting with AgentSpeak(L) Agents

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BDI AgentsBDI Agents

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BDI AgentsBDI Agents

• Why BDI model:Why BDI model:

– Description based on Description based on “mentalistic”“mentalistic” attributes. attributes.

– “ “Humanistic” way to predict agent behaviour.Humanistic” way to predict agent behaviour.

– A “re-usable” abstract model.A “re-usable” abstract model.

– Reactive and deliberative approach.Reactive and deliberative approach.

– Research outcomeResearch outcome• TheoreticalTheoretical

• PracticalPractical

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• Important contribution:Important contribution:

– Represents a large family of BDI systems.Represents a large family of BDI systems.

– An approach to bridge the gap between An approach to bridge the gap between theory and practice.theory and practice.

– Abstraction of successful implemented Abstraction of successful implemented systems PRS and dMars.systems PRS and dMars.

On Experimenting with AgentSpeak(L) Agents

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AgentSpeak(L)AgentSpeak(L)

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On Experimenting with AgentSpeak(L) Agents

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AgentSpeak(L)AgentSpeak(L)• Event: Perceivable characteristics

• External

• Internal

• Beliefs: Knowledge about the world

• Plan Library: Procedural Knowledge

• Intentions: Partial Instantiate Plans

• Actions: Selected for execution

• Select plan

• Select Event

• Select intention Perceived Input

Beliefs ActionsEvents

S Event S IntentionS Plan

Action Output

AGENTSPEAK(L) AGENT

ENVIRONMENT

IntentionsPlan

Library

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I1 I3I2

Event Base Belief Base

Plan Library

SEGenerate Relevant

Plans

Generate Applicable

PlansSP SI

Execute Intention

Action

Plans

Beliefs

Action BasePerception

Intentions

If !Goal then generate Internal

Event

Beliefs

Action

If ?Goal then Unify with Beliefs and Update Selected Intention

AgentSpeak(L): InterpreterAgentSpeak(L): Interpreter

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• Selection functions are non-deterministicSelection functions are non-deterministic

• Programming of only single agent systems.Programming of only single agent systems.

• A goal can be achieved, if an agent:A goal can be achieved, if an agent:

– Has the necessary plansHas the necessary plans

– Can perform all actions in a plan.Can perform all actions in a plan.

• It cannot handle plan failureIt cannot handle plan failure

• Events cannot be “processed” are not removed.Events cannot be “processed” are not removed.

– Not aware about lack of procedural knowledgeNot aware about lack of procedural knowledge

– Redundant computationRedundant computationOn Experimenting with AgentSpeak(L) Agents

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AgentSpeak(L): LimitationsAgentSpeak(L): Limitations

Part III: DemonstrationPart III: Demonstration

Building Agentspeak(L) agents

• Agentspeak(L) implemented in JavaAgentspeak(L) implemented in Java– Constructs: beliefs, plans, goals, intentions all Constructs: beliefs, plans, goals, intentions all

accesible as APIsaccesible as APIs– Agent InterpreterAgent Interpreter– AgentAgent

• Building Agentspeak(L) agents:Building Agentspeak(L) agents:– Implement Implement

• BeliefsBeliefs• EventsEvents• Plans.Plans.

On Experimenting with AgentSpeak(L) Agents

1st Operational Cycle:

-Move to Square(o)

2nd Operational Cycle

-Move to Square(o)

3rd Operational Cycle

Tom knowns there is a waste at square(o)

3st Operational Cycle4th Operational Cycle

- Move to bin @ square(p)

5th Operational Cycle

- Move to bin @ square(p)

6th Operational Cycle

- Move to bin @ square(p)

7th Operational Cycle

- Drop waste into the bin

6st Operational Cycle8th Operational Cycle

Observe the environment for any changes.

ReferencesReferences• M. d’Inverno and M. Luck. Engineering

AgentSpeak(L): A formal computational model. Logic and Computation, 8(3), 1998.

• N. R. Jennings, K. Sycara, and M. Wooldridge. A roadmap of agent research and development. Journal of Autonomous Agents and Multi-Agent Systems, 1(1):7–38, 1998.

• M. E. Bratman, D. Israel, and M. E. Pollack, Plans and resource bounded practical reasoning. Computational Intelligence, 4:349–355, 1988.

• Michael Luck, Peter McBurney, Chris Preist, and Christine Guilfoyle. Agent technology: Enabling next generation computing, 2003.

Augmenting BDI Agent Architectures for Social Reasoning

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ReferencesReferences

• Anand S. Rao. Agentspeak(l): BDI agents speak out in a logical computable language. In Modelling Autonomous Agents in a Multi-Agent World, pages 42–55, 1996.

• Jaime Simao Sichman, Rosaria Conte, Yves Demazeau, and Christiano Castelfranchi. A social reasoning mechanism based on dependence networks. In Michael N. Huhns and Munundar P. Singh, editors, Readings in Agents, pages 416–420, San Francisco, 1997. Morgan Kaufmann.

• M. Wooldridge and N. R. Jennings. Agent Theories, Architectures, and Languages: A Survey. In Jennings Wooldridge, editor, Intelligent Agents, ECAI-94, workshop on Agent theories, Architectures, and Languages (ATAL), number 890 in LNAI, pages 1–39, Berlin, Germany, August 1994. Springer-Verlag.

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