A Markov Chain Approach to Analysis of Cooperation in Multi-Agent Search Missions

David E. Jeffcoat (), Pavlo A. Krokhmal () and Olesya I. Zhupanska ()
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David E. Jeffcoat: Munitions Directorate
Pavlo A. Krokhmal: University of Iowa
Olesya I. Zhupanska: University of Florida

A chapter in Cooperative Systems, 2007, pp 171-184 from Springer

Abstract: Summary We consider the effects of cueing in a cooperative search mission that involves several autonomous agents. Two scenarios are discussed: one in which the search is conducted by a number of identical search-and-engage vehicles, and one where these vehicles are assisted by a search-only (reconnaissance) asset. The cooperation between the autonomous agents is facilitated via cueing, i.e. the information transmitted to the agents by a searcher that has just detected a target. The effect of cueing on the target detection probability is derived from first principles using a Markov chain analysis. Exact solutions to Kolmogorov-type differential equations are presented, and existence of an upper bound on the benefit of cueing is demonstrated.

Keywords: Detection Rate; Transition Rate; Target Detection; Autonomous Agent; Search Capability (search for similar items in EconPapers)
Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-540-48271-0_11

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DOI: 10.1007/978-3-540-48271-0_11

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