Performance Characterization of an Algorithm to Estimate the Search Skill of a Human or Robot Agent
Audrey Balaska, Jason H. Rife · 2020
This paper characterizes an algorithm that estimates searcher skill level to support planning for search activities involving heterogeneous robot and human/robot teams. Specifically, we use Monte-Carlo simulations to determine the empirical accuracy of the estimator, to assess the quality of its predicted distribution (nonparametric) of agent skill levels, and the convergence rate of the estimate. The simulation study suggests that a single challenging search task can be used to estimate searcher skill within about 10%; however, the quality of the estimate is higher when searcher skill is high.