Minimizing communication cost in a distributed Bayesian network using a decentralized MDP

In complex distributed applications, a problem is often decomposed into a set of subproblems that are distributed to multiple agents. We formulate this class of problems with a two layer Bayesian Network. Instead of merely providing a statistical view, we propose a satisficing approach to predict the minimum expected communication needed to reach a desired solution quality. The problem is modelled with a decentralized MDP, and two approximate algorithms are developed to find the near optimal communication strategy for a given problem structure and a required solution quality. Categories and Subject Descriptors I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence—Coherence and coordination, Multiagent systems General Terms Algorithms, Design Keywords coordination of multiple agents, action selection, decentralized MDPs, decision-theoretic planning, Bayesian Networks

• Type: Conference
• Year: 2003
• Where: ATAL
• Authors: Jiaying Shen, Victor R. Lesser, Norman Carver
• Keywords: ATAL 2003, Bayesian Networks, Decentralized Mdps, Multiple Agents