Contribution to multiagent planning for active information gathering

Abstract: In this thesis, we address the problem of performing event exploration. We define event exploration as the process of exploring a topologically known environment to gather information about dynamic events in this environment. Multiagent systems are commonly used for information gathering applications, but bring important challenges such as coordination and communication. This thesis proposes a new fully decentralized model of multiagent planning for information gathering. In this model, called MAPING (Multi-Agent Planning for INformation Gathering ), the agents use an extended belief state that contains not only their own beliefs but also approximations of other agents’ beliefs. With this extended belief state they are able to quantify the relevance of a piece of information for themselves but also for others. They can then decide to explore a specific area or to communicate a specific piece of information according to the action that brings the most information to the system in its totality. The major drawback of this model is its complexity: the size of the belief states space increases exponentially with the number of agents and the size of the environment. To overcome this issue, we also suggest a solving algorithm that uses the well-known adopted assumption of variable independence.Finally we consider the fact that event exploration is usually an open-ended problem. Therefore the agents need to check again their beliefs even after they reached a good belief state. We suggest a smoothing function that enables the agents to forget gradually old observations that can be obsolete.We evaluated our model on different scenarios inspired by real-type applications. These experiments show the ability of MAPING to tackle the event exploration problem with limited communications.