Deceptive Decision-Making Under Uncertainty

• URL: http://arxiv.org/abs/2109.06740v1
• Date: Tue, 14 Sep 2021 14:56:23 GMT
• Title: Deceptive Decision-Making Under Uncertainty
• Authors: Yagiz Savas, Christos K. Verginis, Ufuk Topcu
• Abstract summary: We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks. 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. We propose a novel approach to model observer predictions based on the principle of maximum entropy and to efficiently generate deceptive strategies.
• Score: 25.197098169762356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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