  We study the design of autonomous agents that are capable of deceiving
outside observers about their intentions while carrying out tasks in
stochastic, complex environments. By modeling the agent's behavior as a Markov
decision process, we consider a setting where the agent aims to reach one of
multiple potential goals while deceiving outside observers about its true goal.
We propose a novel approach to model observer predictions based on the
principle of maximum entropy and to efficiently generate deceptive strategies
via linear programming. The proposed approach enables the agent to exhibit a
variety of tunable deceptive behaviors while ensuring the satisfaction of
probabilistic constraints on the behavior. We evaluate the performance of the
proposed approach via comparative user studies and present a case study on the
streets of Manhattan, New York, using real travel time distributions.
