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dc.contributor.authorLin, Kyle Y.
dc.contributor.authorSingham, Dashi I.
dc.dateNovember 2015
dc.date.accessioned2016-01-14T16:32:32Z
dc.date.available2016-01-14T16:32:32Z
dc.date.issued2015-11
dc.identifier.urihttp://hdl.handle.net/10945/47573
dc.descriptionApproved for public release; distribution is unlimited.en_US
dc.descriptionThe previous version has a signature misplaced on Figure 1, which is corrected in this version.en_US
dc.description.abstractIn 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.en_US
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis 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.en_US
dc.titleRobust search policies against an intelligent evaderen_US
dc.typeTechnical Reporten_US
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentOperations Research (OR)
dc.subject.authorsearch theoryen_US
dc.subject.authortwo-person zero-sum gameen_US
dc.subject.authorrobust strategyen_US
dc.identifier.npsreportNPS-OR-15-009


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