Learning of Mediation Strategies for Heterogeneous Agents Cooperation

Romaric Charton 1 Anne Boyer 1 François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Making heterogeneous agents cooperate is still an open problem. We have studied the interaction between a human agent and an information service agent. Our approach is to introduce a mediator agent to formalize the requests of the users, according to their profile and then to give the relevant answers. The mediator must find the best mediation strategy (a sequence of interactions) with a Markov Decision Process (MDP). The states are built on an attribute based referential and the capacity of the source to answer the request under formalization. The actions allow to ask questions to the user or to probe the information source. The rewards reflect the satisfaction of the, the length of the mediations and the quantity of results. Our prototype uses reinforcement learning (Q-Learning) for an on-line adaptation without requiring an a priori model. We describe our experiments on a flight information service with a simulated behaviour.
Type de document :
Communication dans un congrès
Bob Werner. 15th IEEE International Conference on Tools with Artificial Intelligence - ICTAI'2003, 2003, Sacramento, Californie, USA, IEEE Computer Society, pp.330-337, 2003
Liste complète des métadonnées

https://hal.inria.fr/inria-00099588
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 09:39:00
Dernière modification le : jeudi 11 janvier 2018 - 06:19:51

Identifiants

  • HAL Id : inria-00099588, version 1

Collections

Citation

Romaric Charton, Anne Boyer, François Charpillet. Learning of Mediation Strategies for Heterogeneous Agents Cooperation. Bob Werner. 15th IEEE International Conference on Tools with Artificial Intelligence - ICTAI'2003, 2003, Sacramento, Californie, USA, IEEE Computer Society, pp.330-337, 2003. 〈inria-00099588〉

Partager

Métriques

Consultations de la notice

157