programming semantic agents

[email protected] - 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

Universiteit Twente - 19/03/2009 2

What is a MAS

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

Agent are autonomous

Able to interact with their environment

Able to interact with other agents

Definitions vary between MAS subdomains


Several types of agents

Reactive : perceive environment

Proactive : goal directed

Intelligent : Reactive Pro-active Social Ability

Cognitive Knowledge Management Process Reasoning abilities High level representation of environment


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)


Details : BIBSTER


Semantic Agent : why ?

Our goal : Extend agent capabilities by introducing reflection on Reasoning.


Knowledge

Goals

Behaviour

{ Cognitive Agents}+ Behaviour

as Knowledge

=

Semantic Agents

Our contribution What exists

Semantic Web


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


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


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, …


Agent Behaviour Model


State • Begin• A• B• …• End

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

Agent Internal Architecture


Knowledge BASE

Engine

Atomic actions

Knowledge of the agent

Low level implementation

Start RulesExecute Actions

Agent interpreter


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)


Example of behaviour II


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


Implementation layers


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


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 Behaviour

Engine queries SWRL repository through API :NextState value ? Answer : A

A != BEGIN, there is a transitionEngine queries repository to get the Action Sequence

Update the current state to A

For each action Get the name from repository : RegisterDF

Get the parameters : none in this case

Call the low level atomic action with the parameters : execution of the action


Timeline of a transition


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 t

o A

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


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


Questions ?


programming semantic agents

Documents

swrl universiteit twente

irelanduniversiteit

bibsteruniversiteit

knowledge base

mascognitive agents

web semantic technologies

behaviouragent behaviour

semantic web tools