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oai:riuma.uma.es:10630/76972026-02-03T11:31:09Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Nogueira-Collazo, Mariela author Cotta-Porras, Carlos author Fernández-Leiva, Antonio José author 2014-06 The 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. Natural Computing (2014) 13(2):131–144 1572-9796 (online) DOI 10.1007/s11047-014-9411-3 http://hdl.handle.net/10630/7697 Videojuegos Ingeniería del software Virtual player design using self-learning via competitive coevolutionary algorithms