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Formation dynamique d'équipes dans les DEC-POMDPS ouverts à base de méthodes Monte-Carlo

Jonathan Cohen 1
1 Equipe MAD - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : This thesis addresses the problem where a team of cooperative and autonomous agents, working in a stochastic and partially observable environment towards solving a complex task, needs toe dynamically modify its structure during the process execution, so as to adapt to the evolution of the task. It is a problem that has been seldom studied in the field of multi-agent planning. However, there are many situations where the team of agents is likely to evolve over time.We are particularly interested in the case where the agents can decide for themselves to leave or join the operational team. Sometimes, using few agents can be for the greater good. Conversely, it can sometimes be useful to call on more agents if the situation gets worse and the skills of some agents turn out to be valuable assets.In order to propose a decision model that can represent those situations, we base upon the decentralized and partially observable Markov decision processes, the standard model for planning under uncertainty in decentralized multi-agent settings. We extend this model to allow agents to enter and exit the system. This is what is called agent openness. We then present two planning algorithms based on the popular Monte-Carlo Tree Search methods. The first algorithm builds separable joint policies by computing series of best responses individual policies, while the second algorithm builds non-separable joint policies by ranking the teams in each situation via an Elo rating system. We evaluate our methods on new benchmarks that allow to highlight some interesting features of open systems.
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Submitted on : Friday, December 20, 2019 - 11:31:07 AM
Last modification on : Tuesday, February 11, 2020 - 1:38:21 AM
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  • HAL Id : tel-02421055, version 1


Jonathan Cohen. Formation dynamique d'équipes dans les DEC-POMDPS ouverts à base de méthodes Monte-Carlo. Système multi-agents [cs.MA]. Normandie Université, 2019. Français. ⟨NNT : 2019NORMC225⟩. ⟨tel-02421055⟩



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