RAPID: A Belief Convergence Strategy for Collaborating with Inconsistent Agents

Maintaining an accurate set of beliefs in a partially observable scenario, particularly with respect to other agents operating in the same space, is a vital aspect of multiagent planning. We analyze how the beliefs of an agent can be updated for fast adaptivity to changes in the behavior of an unknown teammate. The main contribution of this paper is the empirical evaluation of an agent cooperating with a teammate whose goals change periodically. We test our approach in a collaborative multiagent domain where identification of goals is necessary for successful completion. The belief revision technique we propose outperforms the traditional approach in a majority of test cases. Additionally, our results suggest the ability to approximate a higher level model by utilizing a belief distribution over a set of lower level behaviors, particularly when the belief update strategy identifies changes in the behavior in a responsive manner.