Comparing Heterogeneous and Homogeneous Flocking Strategies for the Ghost Team in the Game of Ms. Pac-Man

F. Liberatore, A. García, P. Castillo, J. J. M. Guervós
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引用次数: 4

Abstract

In the last year, thanks to the Ms. Pac-Man Versus Ghosts Competition, the game of Ms. Pac-Man has gained increasing attention from academics in the field of computational intelligence. In this paper, we contribute to this research stream by presenting a simple genetic algorithm with lexicographic ranking (GALR) for the optimization of flocking strategy-based ghost controllers. Flocking strategies are a paradigm for intelligent agents characterized by showing emergent behavior and for having very little computational and memory requirements, making them well suited for commercial applications and mobile devices. In particular, we study empirically the effect of optimizing homogeneous and heterogeneous teams. The computational analysis shows that the flocking strategy-based controllers generated by the proposed GALR outperform the ghost controllers included in the competition framework and some of those presented in the literature.
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比较《吃豆女士》中幽灵团队的异质和同质群集策略
在过去的一年里,由于“吃豆女士vs鬼”的比赛,“吃豆女士”这款游戏得到了计算智能领域学者们越来越多的关注。在本文中,我们通过提出一种简单的字典排序遗传算法(GALR)来优化基于群集策略的幽灵控制器,从而为这一研究流做出贡献。群集策略是智能代理的一种范例,其特点是表现出紧急行为,并且具有很少的计算和内存需求,使其非常适合商业应用程序和移动设备。特别地,我们实证研究了优化同质团队和异质团队的效果。计算分析表明,本文提出的GALR生成的基于群集策略的控制器优于竞争框架中包含的幽灵控制器和一些文献中提出的控制器。
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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