In many real-world multiagent applications such as distributed sensor nets, a network of agents is formed based on each agent's limited interactions with a small number of neighbors. While distributed POMDPs capture the real-world uncertainty in multiagent domains, they fail to exploit such locality of interaction. Distributed constraint optimization (DCOP) captures the locality of interaction but fails to capture planning under uncertainty. This paper present a new model synthesized from distributed POMDPs and DCOPs, called Networked Distributed POMDPs (ND-POMDPs). Exploiting network structure enables us to present two novel algorithms for ND-POMDPs: a distributed policy generation algorithm that performs local search and a systematic policy search that is guaranteed to reach the global optimal.
|出版ステータス||出版済み - 12 1 2005|
|イベント||20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05 - Pittsburgh, PA, 米国|
継続期間: 7 9 2005 → 7 13 2005
|その他||20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05|
|Period||7/9/05 → 7/13/05|
All Science Journal Classification (ASJC) codes