12th International Conference on Artificial Intelligence: Methodology, Systems, Applications (AIMSA...

12th International Conference on Artificial Intelligence: Methodology, Systems, Applications (AIMSA 2006)

Multiagent Approach for the Representation of

Information in a Decision Support System

Fahem KEBAIR & Frédéric SERINUniversity of le Havre, France

Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS)

Computer Science, Information and Systems Processing Laboratory

AIMSA 2006

Laboratoire d'Informatique de Traitement de l'Information et des Systèmes

LITIS

Research Framework

Decision Support System

Information Representation MultiAgent System

Conclusion

Research FrameworkDecision Support System Information Representation MASConclusion

Plan

2 / 17

Need of Intelligent Systems in an Emergency Situation ?

Constraints of an emergency situation

- limited time and resources

- important mass of information

Research FrameworkDecision Support System Information Representation MASConclusion

Need of Intelligent Systems in an Emergency Situation ?Crisis Management Support System

3 / 17

Role

- helps actors manage crisis cases

- deals with situation change in real time

Characteristics

- intelligent: autonomous and adaptive

How we construct it ?

- intelligent agents and multiagent systems

Research FrameworkDecision Support System Information Representation MASConclusion

Need of Intelligent Systems in an Emergency Situation ?Crisis Management Support System

Crisis Management Support System

4 / 17

Role

Research FrameworkDecision Support System Information Representation MASConclusion

RoleArchitectureCore

Provides a decision-making support

Anticipates the occur of potential incidents

5 / 17

Architecture

Research FrameworkDecision Support System Information Representation MASConclusion

RoleArchitectureCore

USERS

INTERFACE CORE

DIS

DIS

DISOUTSID

EQUERR

YMAS

SCENARIOS BASE

INSIDEQUERR

YMAS

Ontologies

Proximity measures

6 / 17

Core

Research FrameworkDecision Support System Information Representation MASConclusion

RoleArchitectureCore

Level 3 : Prediction Agents

Scenarios

Level 2 : Synthesis Agents

Level 1 : Factual Agents

UsesCharacterisation of the situation

Representation of the situation

Multilayer ArchitectureMultilayer Architecture

Connection between current situation and past

7 / 17

Semantic description of the current situation

Factual agents organisations for information

representation

Environment description based-upon object

paradigm

Information structuring in the form of semantic

features

Research FrameworkDecision Support System Information Representation MASConclusion

PresentationSemantic FeaturesOntologies and Proximity MeasuresFactual AgentsGame of Risk Use Case

8 / 17

Presentation

Semantic Features

Research FrameworkDecision Support System Information Representation MASConclusion

PresentationSemantic FeaturesOntologies and Proximity MeasuresFactual AgentsGame of Risk Use Case

Elementary piece of information

Each semantic feature is related to an object

Form: (key, (qualification, value) )

Example: (phenomenon#1, type, fire, location, #45,

time, 9:33)

9 / 17

Ontologies and Proximity Measures

Research FrameworkDecision Support System Information Representation MASConclusion

Situation formalisationSemantic FeaturesOntologies and Proximity MeasuresFactual AgentsGame of Risk Use Case

Agents communication is based on specific ontologies according to FIPA communicative acts

Proximity measures to compare between two

semantic features: time, spatial and semantic

proximities

use of an ontology of the domain

10 / 17

Factual Agents Structure

Research FrameworkDecision Support System Information Representation MASConclusion

Situation formalisationSemantic FeaturesOntologies and Proximity MeasuresFactual AgentsGame of Risk Use Case

Intelligent agents

Semantic Feature

AutomatonIndicators

Acquaintances Network

11 / 17

Factual Agents Behaviour

Research FrameworkDecision Support System Information Representation MASConclusion

Situation formalisationSemantic FeaturesOntologies and Proximity MeasuresFactual AgentsGame of Risk Use Case

Automaton: ATN (Augmented Transition Network)

DeliberationInitialisation ActionDecision

Indicators: pseudoPosition, pseudoSpeed, pseudoAcceleration, satisfactory and constancy indicators

12 / 17

Game of Risk Use CaseGame UML Representation

Research FrameworkDecision Support System Information Representation MASConclusion

PresentationSemantic FeaturesOntologies and Proximity MeasuresFactual AgentsGame of Risk Use Case

Territory

Continent

name

nameforce

Player

colour

1..*

1

1..*

1

1..*1..*

neighbour

attack

**

13 / 17

Research FrameworkDecision Support System Information Representation MASConclusion

PresentationSemantic FeaturesOntologies and Proximity MeasuresFactual AgentsGame of Risk Use Case

Two types of semantic features:

- territory type: (Quebec, player, green, nbArmies, 4,

time, 4)

- player type: (blue, nbTerritories, 4, time, 4)

two types of factual agents: territory agent and

player agent

Game of Risk Use Case Representation MAS

14 / 17

Research FrameworkDecision Support System Information Representation MASConclusion

PresentationSemantic FeaturesOntologies and Proximity MeasuresFactual AgentsGame of Risk Use Case

(Person 2005 )Game of Risk Use Case Representation MAS Static View

15 / 17

Research FrameworkDecision Support System Information Representation MASConclusion

Results

- semantic dynamic representation of the current

situation

- generic and specific parts composing the system

Perspectives

- connexion with characterisation MAS

- e-learning

- emergency logistics (RoboCup Rescue)

Conclusion

16 / 17

Thank you for your attention

For more information contact us on:

fahem.kebair@univ-lehavre.fr

17 / 17