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We present the Policy and Goal Recognizer (PaGR), a casebased system for multiagent keyhole recognition. PaGR is a knowledge recognition component within a decision-making agent that controls simulated unmanned air vehicles in Beyond Visual Range combat. PaGR stores in a case the goal, observations, and policy of a hostile aircraft, and uses cases to recognize the policies and goals of newly-observed hostile aircraft. In our empirical study of PaGR's performance, we report evidence that knowledge of an adversary's goal improves policy recognition. We also show that PaGR can recognize when its assumptions about the hostile agent's goal are incorrect, and can often correct these assumptions. We show that this ability improves PaGR's policy recognition performance in comparison to a baseline algorithm.

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