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 a distributed policy generation algorithm that performs local search.
|Number of pages||3|
|Journal||IJCAI International Joint Conference on Artificial Intelligence|
|Publication status||Published - Dec 1 2005|
|Event||19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom|
Duration: Jul 30 2005 → Aug 5 2005
All Science Journal Classification (ASJC) codes
- Artificial Intelligence