Approximate Solutions of Interactive POMDPs Using Point Based Value Iteration

We develop a point based method for solving finitely nested interactive POMDPs approximately. Analogously to point based value iteration (PBVI) in POMDPs, we maintain a set of belief points and form value functions composed of only those value vectors that are optimal at these points. However, as we focus on multiagent settings, the beliefs are nested and the computation of the value vectors relies on predicted actions of others. Consequently, we develop an interactive generalization of PBVI applicable to multiagent settings. We bound the error theoretically and provide empirical results using multiple domains.