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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
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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
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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
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Role
Research FrameworkDecision Support System Information Representation MASConclusion
RoleArchitectureCore
Provides a decision-making support
Anticipates the occur of potential incidents
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Architecture
Research FrameworkDecision Support System Information Representation MASConclusion
RoleArchitectureCore
USERS
INTERFACE CORE
DIS
DIS
DISOUTSID
EQUERR
YMAS
SCENARIOS BASE
INSIDEQUERR
YMAS
Ontologies
Proximity measures
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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
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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
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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)
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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
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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
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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
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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
**
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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
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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
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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
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Thank you for your attention
For more information contact us on:
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