Computational Rationalization: The Inverse Equilibrium Problem
This addresses the challenge of predicting agent behavior in multi-agent settings, which is crucial for applications like robotics and AI, but it appears incremental as it extends single-agent inverse optimal control methods to multi-agent contexts.
The paper tackles the problem of modeling purposeful behavior of imperfect agents in competitive and cooperative multi-agent domains from limited observations, introducing a technique based on game-theoretic regret and maximum entropy to predict and generalize behavior.
Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the single-agent decision-theoretic setting, inverse optimal control techniques assume that observed behavior is an approximately optimal solution to an unknown decision problem. These techniques learn a utility function that explains the example behavior and can then be used to accurately predict or imitate future behavior in similar observed or unobserved situations. In this work, we consider similar tasks in competitive and cooperative multi-agent domains. Here, unlike single-agent settings, a player cannot myopically maximize its reward; it must speculate on how the other agents may act to influence the game's outcome. Employing the game-theoretic notion of regret and the principle of maximum entropy, we introduce a technique for predicting and generalizing behavior.