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R. Chandra and M. Zhang, “Cooperative Coevolution of Elman Recurrent Neural Networks for Chaotic Time Series Prediction,” Neurocomputing, Vol. 86, 2012, pp. 116-123. doi:10.1016/j.neucom.2012.01.014

has been cited by the following article:

• TITLE: A Competitive Markov Approach to the Optimal Combat Strategies of On-Line Action Role-Playing Game Using Evolutionary Algorithms

  AUTHORS: Haoyang Chen, Yasukuni Mori, Ikuo Matsuba

  KEYWORDS: Game Design; Game Balance; Competitive Markov Decision Process; Cooperative Coevolutionary Algorithm; Competitive Coevolution

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.4 No.3, August 30, 2012

  ABSTRACT: In the case of on-line action role-playing game, the combat strategies can be divided into three distinct classes, Strategy of Motion(SM), Strategy of Attacking Occasion (SAO) and Strategy of Using Skill (SUS). In this paper, we analyze such strategies of a basic game model in which the combat is modeled by the discrete competitive Markov decision process. By introducing the chase model and the combat assistant technology, we identify the optimal SM and the optimal SAO, successfully. Also, we propose an evolutionary framework, including integration with competitive coevolution and cooperative coevolution, to search the optimal SUS pair which is regarded as the Nash equilibrium point of the strategy space. Moreover, some experiments are made to demonstrate that the proposed framework has the ability to find the optimal SUS pair. Furthermore, from the results, it is shown that using cooperative coevolutionary algorithm is much more efficient than using simple evolutionary algorithm.