Improved use of partial policies for identifying behavioral equivalence

Interactive multiagent decision making often requires to predict actions of other agents by solving their behavioral models from the perspective of the modeling agent. Unfortunately, the general space of models in the absence of constraining assumptions tends to be very large thereby making multiagent decision making intractable. One approach that can reduce the model space is to cluster behaviorally equivalent models that exhibit identical policies over the whole planning horizon. Currently, the state of the art on identifying equivalence of behavioral models compares partial policy trees instead of entire trees. In this paper, we further improve the use of partial trees for the identification purpose and develop an incremental comparison strategy in order to efficiently ascertain the model equivalence. We investigate the improved approach in a well-defined probabilistic graphical model for sequential multiagent decision making - interactive dynamic influence diagrams, and evaluate its performance over multiple problem domains.