A collaborative 20 questions model for target search with human-machine interaction

Theodoros Tsiligkaridis, Brian M. Sadler, A. Hero
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引用次数: 6

Abstract

We consider the problem of 20 questions with noise for collaborative players under the minimum entropy criterion [1] 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 [1, 2] 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-square 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.
基于人机交互的20题目标搜索协同模型
在随机搜索条件下,我们考虑了最小熵准则[1]下的20个带有噪声的协作参与者问题,并将其应用于目标定位。首先,假设条件独立的合作者,我们描述了构建问题序列的最优策略的结构。这推广了随机搜索问题的单人概率平分方法[1,2]。其次,我们证明了一个分离定理,表明最优联合查询与贪婪顺序方案具有相同的性能。第三,我们建立了均方误差(MSE)的收敛速率。第四,给出了序列格式的MSE的上界。该框架为主动机器学习系统提供了一个将人纳入循环的数学模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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