A Collaborative 20 Questions Model for Target Search with Human-Machine Interaction
MICHIGAN UNIV ANN ARBOR DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
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We consider the problem of 20 questions with noise for collaborative players under the minimum entropy criterion in the setting of stochastic search, with application to target localization. First, assuming conditionally independent collaborators, we characterize the structure of the optimal policy for constructing the sequence of questions. This generalizes the single player probabilistic bisection method for stochastic search problems. Second, we prove a separation theorem showing that optimal joint queries achieve the same performance as a greedy sequential scheme. Third, we establish convergence rates of the mean-squared error MSE. Fourth, we derive upper bounds on the MSE of the sequential scheme. This framework provides a mathematical model for incorporating a human in the loop for active machine learning systems.
- Statistics and Probability
- Target Direction, Range and Position Finding
- Human Factors Engineering and Man Machine Systems