MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs

TL;DR

MAA* is a novel heuristic search algorithm that guarantees optimal solutions for decentralized POMDPs with finite horizon, advancing multi-agent planning in uncertain environments.

Contribution

It introduces the first complete and optimal heuristic search method for DEC-POMDPs, combining classical search with decentralized control theory.

Findings

MAA* outperforms existing methods in efficiency and solution quality.

The anytime variant provides solutions with anytime improvements.

Potential extensions include approaches for infinite horizon problems.

Abstract

We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partially-observable Markov decision problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multirobot coordination, network traffic control, `or distributed resource allocation. Solving such problems efiectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA* has significant advantages. We introduce an anytime variant of MAA* and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems.