Abstract
Consider a search problem in which a stationary object is in one of \(L \epsilon \mathcal {N}\) locations. Each location can be searched using one of \(T \epsilon \mathcal {N}\) technologies, and each location-technology pair has a known associated cost and overlook probability. These quantities may depend on the number of times that the technology is applied to the location. This paper finds a search policy that maximizes the probability of finding the object given a constraint on the available budget. It also finds the policy that maximizes the probability of correctly stating at the end of a search where the object is. Additionally it exhibits another policy that minimizes the expected cost required to find the object and the optimal policy for stopping.


Similar content being viewed by others
Change history
31 August 2021
A Correction to this paper has been published: https://doi.org/10.1007/s00186-021-00749-7
References
Agmon N, Kraus S, Kaminka G, Sadov V (2009) Adversarial uncertainty in multi-robot patrol. In: IJCAI’09 proceedings of the 21st international joint conference on artificial intelligence. pp 1811–1817
Alpern S, Fokkink R, Gasieniec L, Lindelauf R, Subrahmanian V (eds) (2013) Search theory: a game theoretic perspective. Springer, Heidelberg
Babichenko Y, Peres Y, Peretz R, Sousi P, Winkler P (2014) Hunter, Cauchy rabbit and optimal Kakeya sets. Trans Am Math Soc 366(10):5567–5586
Bellman R (1957) Dynamic programming. Princeton University Press, Princeton
Benkoski S, Monticino M, Weisinger J (1991) A survey of the search theory literature. Nav Res Log 38(4):469–494
Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge
Brown S (1980) Optimal search for a moving target in discrete time and space. Oper Res 28:1275–1289
DeGroot M (1970) Optimal statistical decisions. McGraw-Hill, New York
DeGroot M (1986) Probability and statistics, 2nd edn. Addison-Wesley, Reading
DeGroot M (2004) Optimal statistical decisions. Wiley, Hoboken (reprinted)
Dumitriu I, Tetali P, Winkler P (2003) On playing golf with two balls. SIAM J Discrete Math 16(4):604–615
Gal S (1980) Search games. Academic Press, New York
Gal S, Alpern S (2003) The theory of search and rendezvous. Kluwer Acadmic Publishers, Boston
Gittins J (1979) Bandit processes and dynamic allocation indices (with discussion). J R Stat Soc Ser B 41:148–177
Gittins J, Glazebrook K, Weber R (2011) Multi-armed bandit allocation indices. Wiley, Chichester
Kadane J (1968) Discrete search and the Neyman–Pearson lemma. J Math Anal Appl 22:156–171
Kadane J (1971) Optimal whereabouts search. Oper Res 19:894–904
Koopman B (1956a) The theory of search, part I, kinematic bases. Oper Res 4:324–346
Koopman B (1956b) The theory of search, part II, target detection. Oper Res 4:503–531
Koopman B (1957) The theory of search, part III, the optimum distribution of search effort. Oper Res 5:613–626
Kress M, Lin K, Szechtman R (2008) Optimal discrete search with imperfect specificity. Math Methods Oper Res 68(3):539–549
Kriheli B, Levner E (2013) Search and detection of failed components in repairable complex systems under imperfect inspections. In: Batyrshin I, Mendoza M (eds) Advances in computational intelligence, vol 7630. Lecture notes in computer science, pp 399–410
Lehmann E (1959) Testing statistical hypotheses. Wiley, New York
Mela D (1961) Information theory and search theory as special cases of decision theory. Oper Res 9:907–909
Nakai T (1995) Dynamical and game-theoretial approaches to an optimal patrol problem. J Inf Optim Sci 16(3):491–500
Qingxin Z, Oommen J (1997) On the optimal search problem: the case when the target distribution is unknown. In: Proceedings of the XVII international conference of the Chilean Computer Science Society. pp 268–277
Song NO, Teneketzis D (2004) Discrete search with multiple sensors. Math Methods Oper Res 60(1):1–13
Stone L (1975) Theory of optimal search. Academic Press, New York
Stone L, Streit R, Corwin T, Bell K (2014) Bayesian mutiple target tracking. Artech House, Boston
Tognetti K (1968) An optimal strategy for whereabouts search. Oper Res 16:209–211
Acknowledgments
The author thanks Caroline Mitchell of the Allegheny Mountain Rescue Group for bringing this problem to his attention, and Charles Twardy and two referees for helpful comments.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kadane, J.B. Optimal discrete search with technological choice. Math Meth Oper Res 81, 317–336 (2015). https://doi.org/10.1007/s00186-015-0499-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00186-015-0499-8