Julien.subercaze@liris.cnrs.fr - http://liris.cnrs.fr/julien.subercaze/

Laboratoire d'InfoRmatique en Image et Systèmes d'informationLIRIS UMR 5205 CNRS/INSA de Lyon/Université Claude Bernard Lyon 1/Université Lumière Lyon 2/Ecole Centrale de Lyon

Université Claude Bernard Lyon 1, bâtiment Nautibus43, boulevard du 11 novembre 1918 — F-69622 Villeurbanne cedex

http://liris.cnrs.fr

UMR 5205

Universiteit Twente - 19/03/2009

Universiteit Twente - 19/03/2009

Programming Semantic Agents

Julien Subercaze

Summary

Introduction to MAS

Cognitive agents – a state of the art

Restrictions on cognitive agents

Semantic Agents

Implementation

Conclusion

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What is a MAS

Multi Agent System is useful paradigm for Distributed Artificial Intelligence an for Distributed Knowledge Management

Agent are autonomousAble to interact with their environmentAble to interact with other agents

Definitions vary between MAS subdomains

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Several types of agents

Reactive : perceive environmentProactive : goal directedIntelligent :

Reactive Pro-active Social Ability

Cognitive Knowledge Management Process Reasoning abilities High level representation of environment

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Cognitive Agents

Our scope : Cognitive Agents

Architectures of cognitive agents, related work: Not implemented : IDA (Intelligent Distribution Agent, U of

Memphis)

Dedicated applications in which Knowledge Base and algorithm for behaviours are decoupled :

Bibster : bibliographic references search (AIFB, Germany) and its clones : SemreX and SocioBiblog

Sifo-Peers : Decentralized Social Network (DERI, Ireland)

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Details : BIBSTER

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Semantic Agent : why ?Our goal : Extend agent capabilities by introducing reflection on Reasoning.

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Knowledge

Goals

Behaviour

{ Cognitive Agents}+ Behaviour

as Knowledge

=

Semantic Agents

Our contribution What exists

Semantic Web

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Semantic Web Layers – Tim Berners Lee

Reasoning and Behaviour

Agent behaviour is a part of its knowledge

Technology used : Nowadays, Web Semantic Technologies is the most

advanced to represent technologies OWL stores knowledge SWRL gives extension to express rules on OWL

Our Goal (refined)

Build agents that have their behaviours expressed in SWRL

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Building behaviour representation

Agent Behaviour : sequence of actions

Triggered by in internal/external agents

We can represent the behaviour as extended finite state machine :

Transitions triggered by if statement

Transition => execution of specified actions

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Agent model

Defining the internal agent architecture

List of possible atomic actions Internal :

Add/Remove/Modify : Class, Property, Individual, Rule External :

Receive Messages

These actions have parameters : receiver, content, …

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Agent Behaviour Model

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State • Begin• A• B• …• End

Actions :• Add Property• Remove Property• Add Individual• ….• Send Message• Receive Message

Agent Internal Architecture

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Knowledge BASE

Engine

Atomic actions

Knowledge of the agent

Low level implementation

Start RulesExecute Actions

Agent interpreter

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Engine : Fire Rules

Clock

Execute actions sequence

incoming messages

outgoing messages

If state updated

Knowledge Base Update : • Behaviour• Knowledge• State

Rules and stateActions names and parameters

Knowledge BaseRules – States – Messages-

Knowledge

Example of behaviour

Simple behaviour : Register to the yellow pages Send a query to the agent « Bob » Wait for answer

Use of atomical actions (12 in total): RegisterDF SendMessage WaitForMessage …

Each action takes parameters(name/value)

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Example of behaviour II

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Begin A B{RegisterDF} {SendMessage}

Param Value

Performative Query

Content « Select ?x ..  »

SendTo «Bob»

C{Wait4Message}

END

{AddIndividual}

Implementation

JAVA Based Prototype using following libs:

JADE : MAS Framework

Protege-OWL

JENA Framework

Pellet Reasoner

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Implementation layers

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Knowledge Base

Engine

Atomic actions

Knowledge of the agent

Low level implementation

Start RulesExecute Actions

OWL-DL SWRL

JAVASWRL APIJENA APIProtege API

JAVAJade API

Example of behaviour II

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Begin A B{RegisterDF} {SendMessage}

Param Value

Performative Query

Content « Select ?x ..  »

SendTo «Ralf»

C{Wait4Message}

END

{AddIndividual}

State(?x) hasStateValue(?x,BEGIN) NextState(?y)∧ ∧=> hasStateValue(?y,A) hasValue(Action,RegisterDF) ∧

Execution of BehaviourEngine queries SWRL repository through API :

NextState value ? Answer : A

A != BEGIN, there is a transitionEngine queries repository to get the Action SequenceUpdate the current state to A

For each action Get the name from repository : RegisterDFGet the parameters : none in this caseCall the low level atomic action with the parameters : execution of the action

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Timeline of a transition

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KB

Engine

AtomicActions

A

Nex

tSta

te

Val

ue ?

Act

ionL

ist

Val

ue ?

{Reg

iste

rDF}

Exe

cute

R

egis

er D

F

Message to DF

Upd

ate

Sta

te to

A

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ConclusionWhy : Cognitive Agents implementation always hardcodes behaviour of agents !

Our Solution : Behaviour is part of Knowledge, we describe it using Semantic Web Rules and also defined an agent architecture.

Advantages Reflection on the behaviour of the agent Behaviour is a piece of agents knowledge Behaviour no more depends from agents implementation

language

DrawbacksProgramming is difficult

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Perspectives :

Opens a new way of agent capabilities !! What are the possible applications ? Domains of interest ?

How to exchange Agents Behaviours ?

Use of Virtual Knowledge Communities appears to be the most common solution since agents already exchange knowledge within the communities.

We need an IDE to program Agents

Consistency Checking required

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Questions ?

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