MA-DBN: Modeling Cooperative Agents for Approximate Online Monitoring

Cooperative agents often need to reason about the states of a large and complex uncertain domain that evolves over time. Since exact calculation is usually impractical, we aim at providing a modeling tool that supports approximate online monitoring in such settings. Our proposed framework, the multi-agent dynamic Bayesian networks (MA-DBNs), models the dynamics of a group of cooperative agents approximately by utilizing weak interaction among them. Each dynamic agent maintains an individual chain of evolution, which enables a factorized and more efficient calculation of cooperative online monitoring. Meanwhile, agents are organized by an underlying hypertree structure to facilitate inter-agent communication. The error resulting from our model approximation is expected to be bounded over time, and a re-factorization method is proposed to improve the approximation quality. Moreover, MA-DBNs are flexible in admitting existing BN monitoring techniques for each agent´s local evolution. As an example, we present an algorithm of distributed particle filters under our proposed model.