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Information-Driven Search Strategies in the Board Game of CLUE ^{\circ\it{R}}

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2 Author(s)
Ferrari, S. ; Dept. of Mech. Eng. & Mater. Sci., Duke Univ., Durham, NC ; Chenghui Cai

This paper presents an information-driven sensor management problem, referred to as treasure hunt, which is relevant to mobile-sensor applications such as mine hunting, monitoring, and surveillance. The objective is to infer a hidden variable or treasure by selecting a sequence of measurements associated with multiple fixed targets distributed in the sensor workspace. The workspace is represented by a connectivity graph, where each node represents a possible sensor deployment, and the arcs represent possible sensor movements. An additive conditional entropy reduction function is presented to efficiently compute the expected benefit of a measurement sequence over time. Then, the optimal treasure hunt strategy is determined by a novel label-correcting algorithm operating on the connectivity graph. The methodology is illustrated through the board game of CLUEreg, which is shown to be a benchmark example of the treasure hunt problem. The game results show that a computer player implementing the strategies developed in this paper outperforms players implementing Bayesian networks, Q-learning, or constraint satisfaction, as well as human players.

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:39 ,  Issue: 3 )