Bayesian Belief Update in Antiair Defense

This research applies Bayesian learning for belief update to the antiair defense domain, in which an automated defense unit is to defend a speciied territory from a number of attacking missiles. Bayesian learning enables an agent to adjust his beliefs about the possible models of the other agents, given the observation of their behaviors. Through the Recursive Modeling Method (RMM), agent can select his rational action by examining the expected utility of his alternative behaviors and coordinate with other agent by modeling their decision making in a distributed multiagent environment. Bayesian learning is used in conjunction with RMM for belief update. As a result, an agent can predict which models of the other agents are correct, and keep his knowledge up to date. We describe how Bayesian learning can be used in conjunction with RMM for decision making and coordination in the antiair defense domain and show experimental results.