Robust search policies against an intelligent evader
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Authors
Lin, Kyle Y.
Singham, Dashi I.
Subjects
search theory
two-person zero-sum game
robust strategy
two-person zero-sum game
robust strategy
Advisors
Date of Issue
2015-11
Date
November 2015
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
In a classical search model, an object is hidden in one of many cells. Knowing the probability that the object is in each cell, a searcher
wishes to find it. Each search in a cell incurs a cost and will discover the object with some probability, with both the cost and
discovery probability dependent on the cell. This paper revisits this search problem with an intelligent evader who decides where to
hide in order to evade the search. We make two contributions to the literature. First, we show how to compute a randomized policy
for the searcher to minimize the expected cost until discovering the evader. Second, if the search has to stop at some point, with the
deadline unannounced in advance, we show how the searcher can sequentially allocate each search to simultaneously maximize the
probability of discovering the evader by an arbitrary deadline. In the case where the search cost is identical for all cells, our analysis
shows that the latter policy is more robust.
Type
Technical Report
Description
The previous version has a signature misplaced on Figure 1, which is corrected in this version.
Series/Report No
Department
Operations Research (OR)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
NPS-OR-15-009
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.