RT Journal Article
T1 Virtual player design using self-learning via competitive coevolutionary algorithms
A1 Nogueira-Collazo, Mariela
A1 Cotta-Porras, Carlos
A1 Fernández-Leiva, Antonio José
K1 Videojuegos
K1 Ingeniería del software
AB The Google Artificial Intelligence (AI) Challengeis 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 entriesare 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 primealgorithm 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 alsoconclude 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.
PB Springer
SN 1572-9796 (online)
YR 2014
FD 2014-06
LK http://hdl.handle.net/10630/7697
UL http://hdl.handle.net/10630/7697
LA eng
NO Natural Computing (2014) 13(2):131–144
NO This work is partially supported by SpanishMICINN 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.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 26 feb 2026