Approximating Behavioral Equivalence of Models Using Top-K Policy Paths:(Extended Abstract)

Decision making and game play in multiagent settings must oftencontend with behavioral models of other agents in order to predicttheir actions. One approach that reduces the complexity of the unconstrainedmodel space is to group models that tend to be behaviorallyequivalent. In this paper, we seek to further compress themodel space by introducing an approximate measure of behavioralequivalence and using it to group models.Decision making and game play in multiagent settings must often contend with behavioral models of other agents in order to predict their actions. One approach that reduces the complexity of the unconstrained model space is to group models that tend to be behaviorally equivalent. In this paper, we seek to further compress the model space by introducing an approximate measure of behavioral equivalence and using it to group models.