Letting loose a SPIDER on a network of POMDPs: Generating quality guranteed policies
Conference Proceeding Article
Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are a popular approach for modeling multi-agent systems acting in uncertain domains. Given the significant complexity of solving distributed POMDPs, particularly as we scale up the numbers of agents, one popular approach has focused on approximate solutions. Though this approach is efficient, the algorithms within this approach do not provide any guarantees on solution quality. A second less popular approach focuses on global optimality, but typical results are available only for two agents, and also at considerable computational cost. This paper overcomes the limitations of both these approaches by providing SPIDER, a novel combination of three key features for policy generation in distributed POMDPs: (i) it exploits agent interaction structure given a network of agents (i.e. allowing easier scale-up to larger number of agents); (ii) it uses a combination of heuristics to speedup policy search; and (iii) it allows quality guaranteed approximations, allowing a systematic tradeoff of solution quality for time. Experimental results show orders of magnitude improvement in performance when compared with previous global optimal algorithms.
Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering
Intelligent Systems and Decision Analytics
Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multi Agent Systems, AAMAS
VARAKANTHAM, Pradeep Reddy; Marecki, Janusz; Yokoo, Makoto; and Tambe, Milind.
Letting loose a SPIDER on a network of POMDPs: Generating quality guranteed policies. (2007). Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multi Agent Systems, AAMAS. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/947