Conference Proceeding Article
Decentralized POMDPs provide an expressive framework for sequential multi-agent decision making. Despite their high complexity, there has been significant progress in scaling up existing algorithms, largely due to the use of point-based methods. Performing point-based backup is a fundamental operation in state-of-the-art algorithms. We show that even a single backup step in the multi-agent setting is NP-Complete. Despite this negative worst-case result, we present an efficient and scalable optimal algorithm as well as a principled approximation scheme. The optimal algorithm exploits recent advances in the weighted CSP literature to overcome the complexity of the backup operation. The polytime approximation scheme provides a constant factor approximation guarantee based on the number of belief points. In experiments on standard domains, the optimal approach provides significant speedup (up to 2 orders of magnitude) over the previous best optimal algorithm and is able to increase the number of belief points by more than a factor of 3. The approximation scheme also works well in practice, providing near-optimal solutions to the backup problem.
Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering
Intelligent Systems and Decision Analytics
International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
KUMAR, Akshat and Zilberstein, S..
Point-Based Backup for Decentralized POMPDs: Complexity and New Algorithms. (2010). International Conference on Autonomous Agents and Multiagent Systems (AAMAS). 1, 1315-1322. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2210