Achieving goals in decentralized POMDPs

Coordination of multiple agents under uncertainty in the decentralized POMDP model is known to be NEXP-complete, even when the agents have a joint set of goals. Nevertheless, we show that the existence of goals can help develop effective planning algorithms. We examine an approach to model these problems as indefinite-horizon decentralized POMDPs, suitable for many practical problems that terminate after some unspecified number of steps. Our algorithm for solving these problems is optimal under some common assumptions – that terminal actions exist for each agent and rewards for non-terminal actions are negative. We also propose an infinite-horizon approximation method that allows us to relax these assumptions while maintaining goal conditions. An optimality bound is developed for this samplebased approach and experimental results show that it is able to exploit the goal structure effectively. Compared with the state-of-the-art, our approach can solve larger problems and produce...

• Type: Conference
• Year: 2009
• Where: ATAL
• Authors: Christopher Amato, Shlomo Zilberstein
• Keywords: Artificial Intelligence, ATAL 2009, Decentralized Pomdp Model, Decentralized Pomdps, Problems