Coordination and belief update in a distributed anti-air environment

The research addresses rational decision making and coordination among automated and human controlled anti-air units whose mission is to defend a specified territory from a number of attacking missiles. The automated units have to decide which missiles to attempt to intercept, given the characteristics of the threat, and given the other units' anticipated actions, in their attempt to minimize the expected overall damages to the defended territory. Thus, an automated defense unit needs to model the other agents, either human or automated, that control the other defense batteries. We assume that the units cannot communicate among themselves, say, due to an imposed radio silence. We use the Recursive Modeling Method (RMM), which enables an agent to select his rational action by examining the expected utility of his alternative behaviors, and to coordinate with other agents by modeling their decision making in a multiagent environment. Bayesian learning is used in conjunction with RMM for belief update. As a result, an agent can determine which models of the other agents are correct, and keep his knowledge up to date. We describe how decision making using RMM and Bayesian learning is applied to the anti-air defense domain and show experimental results.