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dc.contributor.authorNogueira Collazo, Mariela
dc.contributor.authorCotta, Carlos 
dc.contributor.authorFernández-Leiva, Antonio J.
dc.date.accessioned2014-06-19T11:01:08Z
dc.date.available2014-06-19T11:01:08Z
dc.date.created2014-06
dc.date.issued2014-06
dc.identifier.citationNatural Computing (2014) 13(2):131–144es_ES
dc.identifier.issn1572-9796 (online)
dc.identifier.otherDOI 10.1007/s11047-014-9411-3
dc.identifier.urihttp://hdl.handle.net/10630/7697
dc.description.abstractThe Google Artificial Intelligence (AI) Challenge is an international contest the objective of which is to program the AI in a two-player real time strategy (RTS) game. This AI is an autonomous computer program that governs the actions that one of the two players executes during the game according to the state of play. The entries are evaluated via a competition mechanism consisting of two-player rounds where each entry is tested against others. This paper describes the use of competitive coevolutionary (CC) algorithms for the automatic generation of winning game strategies in Planet Wars, the RTS game associated with the 2010 contest. Three different versions of a prime algorithm have been tested. Their common nexus is not only the use of a Hall-of-Fame (HoF) to keep note of the winners of past coevolutions but also the employment of an archive of experienced players, termed the hall-of-celebrities (HoC), that puts pressure on the optimization process and guides the search to increase the strength of the solutions; their differences come from the periodical updating of the HoF on the basis of quality and diversity metrics. The goal is to optimize the AI by means of a self-learning process guided by coevolutionary search and competitive evaluation. An empirical study on the performance of a number of variants of the proposed algorithms is described and a statistical analysis of the results is conducted. In addition to the attainment of competitive bots we also conclude that the incorporation of the HoC inside the primary algorithm helps to reduce the effects of cycling caused by the use of HoF in CC algorithms.es_ES
dc.description.sponsorshipThis work is partially supported by Spanish MICINN under Project ANYSELF (TIN2011-28627-C04-01),3 by Junta de Andalucía under Project P10-TIC-6083 (DNEMESIS) and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.ispartofseriesNatural Computing;13
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectVideojuegoses_ES
dc.subjectIngeniería del softwarees_ES
dc.subject.otherCoevolutiones_ES
dc.subject.otherCompetitiones_ES
dc.subject.otherSelf-learninges_ES
dc.subject.otherVirtual playeres_ES
dc.titleVirtual player design using self-learning via competitive coevolutionary algorithmses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES


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