@INPROCEEDINGS{thomas04b,
       CRINNUMBER = {A04-R-109},
       CATEGORY = {3},
       EQUIPE = {MAIA},
       AUTHOR  = {Thomas, Vincent and Bourjot, Christine and Chevrier, Vincent},
       TITLE = {Interac-DEC-MDP\,: Towards the use of interactions in DEC-MDP},
       BOOKTITLE = {{Third International Joint Conference on Autonomous Agents and Multi-Agent Systems - AAMAS'04, New York, USA}},
       YEAR ={ 2004},
       PAGES = {1450-1451},
       MONTH ={ Jul},
       KEYWORDS  = {markov decision process, multi-agent system, interaction, learning},
       ABSTRACT  = {This article presents a new formalism Interac-DEC-MDP whose aim is to introduce the concept of interaction in Decentralized Markov Decision Process and which has been inspired by biology. The aim of this formalism, Interac-DEC-MDP, is to describe and represent interactions among agents. The outcome of interactions is decided collectively by two agents and is in charge of the distribution of local rewards. We have modeled a biological experiment  within this formalism. A simple learning algorithm applied on this formalism generates a more efficient collective behavior than without interactions.},
}