How to Exhibit More Predictable Behaviors

This paper looks at predictability problems, that is, wherein an agent must choose its strategy in order to optimize the predictions that an external observer could make. We address these problems while taking into account uncertainties in the environment dynamics and the observed agent’s policy. To that end, we assume that the observer (1) seeks to predict the agent’s future action or state at each time step, and (2) models the agent using a stochastic policy computed from a known underlying problem, and we leverage on the framework of observer-aware Markov decision processes (OAMDPs). We design reward functions for the agent which encode her goal to make next states or actions predictable by the observer; show that these induced predictable OAMDPs can be represented by goal-oriented or discounted MDPs; and analyze the properties of the proposed reward functions theoretically. Experiments have been conducted, generating and interpreting policies on two types of grid-world problems, and then confronting human observers to these policies on some of these problems.