Deceptive Decision-Making Under Uncertainty
  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.
