A Markov chain approach to analysis of cooperation in multi-agent search missions

David E. Jeffcoat, Pavlo A. Krokhmal, Olesya I. Zhupanska

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationCooperative Systems
Subtitle of host publicationControl and Optimization
EditorsDon Grundel, Panos Pardalos, Robert Murphey, Oleg Prokopyev
Pages171-184
Number of pages14
DOIs
StatePublished - Dec 1 2007
Externally publishedYes

Publication series

NameLecture Notes in Economics and Mathematical Systems
Volume588
ISSN (Print)0075-8442

ASJC Scopus subject areas

  • Mathematics (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)

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