An online POMDP algorithm for complex multiagent environments

In this paper, we present an online method for POMDPs, called RTBSS (Real-Time Belief Space Search), which is based on a look-ahead search to find the best action to execute at each cycle in an environment. We thus avoid the overwhelming complexity of computing a policy for each possible situation. By doing so, we show that this method is particularly efficient for large real-time environments where offline approaches are not applicable because of their complexity. We first describe the formalism of our online method, followed by some results on standard POMDPs. Then, we present an adaptation of our method for a complex multiagent environment and results showing its efficiency in such environments. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search; I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence—Multiagent systems General Terms Algorithms, Experimentation Keywords POMDP, Online Search

• Type: Conference
• Year: 2005
• Where: ATAL
• Authors: Sébastien Paquet, Ludovic Tobin, Brahim Chaib-draa
• Keywords: Artificial Intelligence, ATAL 2005, Belief Space Search, Online Method