Bayesian update of recursive agent models

We present a framework for Bayesian updating of beliefs about models of agent(s) based on their observed behavior. We work within the formalism of the Recursive Modeling Method (RMM) that maintains and processes models an agent may use to interact with other agent(s), the models the agent may think the other agent has of the original agent, the models the other agent may think the agent has, and so on. The beliefs about which model is the correct one are incrementally updated based on the observed behavior of the modeled agent and, as the result, the probability of the model that best predicted the observed behavior is increased. Analogously, the models on deeper levels of modeling can be updated; the models that the agent thinks another agent uses to model the original agent are revised based on how the other agent is expected to observe the original agent's behavior, and so on. We have implemented and tested our method in two domains, and the results show a marked improvement in the quality of interactions with the belief update in both domains.