Maximizing Plan Legibility in Stochastic Environments

Legible behavior allows an observing agent to infer the intention of an observed agent. Producing legible behavior is crucial for successful multi-agent interaction in many domains. We introduce techniques for legible planning in stochastic environments. Maximizing legibility, however, presents a complex trade-off between maximizing the underlying rewards. Hence, we propose a method to balance the trade-off. In our experiments, we demonstrate that maximizing legibility results in unambiguous behaviors.