Towards Action Prediction Using a Mental-Level Model

We propose a formal approach to the problem of prediction based on the following steps: First, a mental-level model is constructed based on the agent's previous actions; next, the model is updated to account for any new observations by the agent, and finally, we predict the optimal action w.r.t. the agent's mental state as its next action. This paper formalizes this prediction process. In order to carry out this process, we need to understand how a mental state can be ascribed to an agent and how this mental state should be updated. In [ Brafman and Tennenholtz, 1994b ] , we examined the first stage. Here we investigate a particular update operator and show that its ascription requires making only weak modeling assumptions. 1 Introduction Tools for representing information about other agents are crucial in many contexts. Often, the goal of maintaining such information is to facilitate prediction of other agents' behavior, so that we can function better in their presence. Mental-level ..