1. 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 SERIN University 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

2. Research Framework Decision Support System Information Representation MultiAgent System Conclusion Research Framework Decision Support System Information Representation MAS Conclusion Plan 2 / 17

3. Need of Intelligent Systems in an Emergency Situation ? Constraints of an emergency situation - limited time and resources - important mass of information Research Framework Decision Support System Information Representation MAS Conclusion Need of Intelligent Systems in an Emergency Situation ? Crisis Management Support System 3 / 17

4. 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 Framework Decision Support System Information Representation MAS Conclusion Need of Intelligent Systems in an Emergency Situation ? Crisis Management Support System 4 / 17

5. Role Research Framework Decision Support System Information Representation MAS Conclusion Role Architecture Core Provides a decision-making support Anticipates the occur of potential incidents 5 / 17

6. Architecture Research Framework Decision Support System Information Representation MAS Conclusion Role Architecture Core USERS INTERFACE COR E DIS OUTSID E QUERR Y MAS SCENARIOS BASE INSIDE QUERR Y MAS Ontologies Proximity measures 6 / 17

7. Core Research Framework Decision Support System Information Representation MAS Conclusion Role Architecture Core Level 3 : Prediction Agents Scenarios Level 2 : Synthesis Agents Level 1 : Factual Agents Uses Characterisation of the situation Representation of the situation Multilayer Architecture Connection between current situation and past 7 / 17

8. 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 Framework Decision Support System Information Representation MAS Conclusion Presentation Semantic Features Ontologies and Proximity Measures Factual Agents Game of Risk Use Case 8 / 17 Presentation

9. Semantic Features Research Framework Decision Support System Information Representation MAS Conclusion Presentation Semantic Features Ontologies and Proximity Measures Factual Agents Game 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

10. Ontologies and Proximity Measures Research Framework Decision Support System Information Representation MAS Conclusion Situation formalisation Semantic Features Ontologies and Proximity Measures Factual Agents Game 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

11. Factual Agents Structure Research Framework Decision Support System Information Representation MAS Conclusion Situation formalisation Semantic Features Ontologies and Proximity Measures Factual Agents Game of Risk Use Case Intelligent agents Semantic Feature Automaton Indicators Acquaintances Network 11 / 17

12. Factual Agents Behaviour Research Framework Decision Support System Information Representation MAS Conclusion Situation formalisation Semantic Features Ontologies and Proximity Measures Factual Agents Game of Risk Use Case Automaton: ATN (Augmented Transition Network) Deliberation Initialisation Action Decision Indicators: pseudoPosition, pseudoSpeed, pseudoAcceleration, satisfactory and constancy indicators 12 / 17

13. Game of Risk Use Case Game UML Representation Research Framework Decision Support System Information Representation MAS Conclusion Presentation Semantic Features Ontologies and Proximity Measures Factual Agents Game of Risk Use Case Territory Continent name force Player colour 1.. * 1 1 neighbour attack * * 13 / 17

14. Research Framework Decision Support System Information Representation MAS Conclusion Presentation Semantic Features Ontologies and Proximity Measures Factual Agents Game 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

15. Research Framework Decision Support System Information Representation MAS Conclusion Presentation Semantic Features Ontologies and Proximity Measures Factual Agents Game of Risk Use Case (Person 2005 ) Game of Risk Use Case Representation MAS Static View 15 / 17

16. Research Framework Decision Support System Information Representation MAS Conclusion 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

17. Thank you for your attention For more information contact us on: fahem.kebair@univ-lehavre.fr 17 / 17