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This is a distributed multiagent decision problem and several different possible solutions have already been proposed. However, most solutions require some sort of central coordination. In this paper, we intend to model each player as a learning automaton and let them evolve and adapt in order to solve the difficult problem they have at hand. We are also going to show that, using the proposed learning process, the players\u2019 policies will converge to an equilibrium point. Simulations of such scenarios with multiple pursuers and evaders are presented in order to show the feasibility of the approach.<\/jats:p>","DOI":"10.1177\/1059712314526261","type":"journal-article","created":{"date-parts":[[2014,5,21]],"date-time":"2014-05-21T01:17:33Z","timestamp":1400635053000},"page":"221-234","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["Decentralized strategy selection with learning automata for multiple pursuer\u2013evader games"],"prefix":"10.1177","volume":"22","author":[{"suffix":"Jr","given":"Sidney N","family":"Givigi","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Royal Military College of Canada, Kingston, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Howard M","family":"Schwartz","sequence":"additional","affiliation":[{"name":"Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2014,5,20]]},"reference":[{"key":"bibr1-1059712314526261","volume-title":"Dynamic noncooperative game theory","author":"Basar T.","year":"1999","edition":"2"},{"key":"bibr2-1059712314526261","volume-title":"Dynamic programming and optimal control","author":"Bertsekas D. 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