A PGM framework for recursive modeling of players in simple sequential Bayesian games

Abstract

We consider the situation where two agents try to solve each their own task in a common environment. In particular, we study simple sequential Bayesian games with unlimited time horizon where two players share a visible scene, but where the tasks (termed assignments) of the players are private information. We present an influence diagram framework for representing simple type of games, where each player holds private information. The framework is used to model the analysis depth and time horizon of the opponent and to determine an optimal policy under various assumptions on analysis depth of the opponent. Not surprisingly, the framework turns out to have severe complexity problems even in simple scenarios due to the size of the relevant past. We propose two approaches for approximation. One approach is to use Limited Memory Influence Diagrams (LIMIDs) in which we convert the influence diagram into a set of Bayesian networks and perform single policy update. The other approach is information enhancement, where it is assumed that the opponent in a few moves will know your assignment. Empirical results are presented using a simple board game.