  A human-centered robot needs to reason about the cognitive limitation and
potential irrationality of its human partner to achieve seamless interactions.
This paper proposes an anytime game-theoretic planner that integrates iterative
reasoning models, a partially observable Markov decision process, and
chance-constrained Monte-Carlo belief tree search for robot behavioral
planning. Our planner enables a robot to safely and actively reason about its
human partner's latent cognitive states (bounded intelligence and
irrationality) in real-time to maximize its utility better. We validate our
approach in an autonomous driving domain where our behavioral planner and a
low-level motion controller hierarchically control an autonomous car to
negotiate traffic merges. Simulations and user studies are conducted to show
our planner's effectiveness.
