A Distributed Decision-Theoretic Model for Multiagent Active Information Gathering
Résumé
Multirobot systems have made tremendous progress in ex-ploration and surveillance. In that kind of problem, agents are not required to perform a given task but should gather as much information as possible. However, information gather-ing tasks usually remain passive. In this paper, we present a multirobot model for active information gathering. In this model, robots explore, assess the relevance, update their be-liefs and communicate the appropriate information to rel-evant robots. To do so, we propose a distributed decision process where a robot maintains a belief matrix representing its beliefs and beliefs about the beliefs of the other robots. This decision process uses entropy and Kullback-Leibler in a reward function to access the relevance of their beliefs and the divergence with each other. This model allows the derivation of a policy for gathering information to make the entropy low and a communication policy to reduce the di-vergence. An experimental scenario has been developed for an indoor information gathering mission.
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