A Hyperheuristic Methodology to Generate Adaptive Strategies for Games

Q2 Computer Science

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-03-01 DOI:10.1109/TCIAIG.2015.2394780

Jiawei Li, G. Kendall

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引用次数: 18

Abstract

Hyperheuristics have been successfully applied in solving a variety of computational search problems. In this paper, we investigate a hyperheuristic methodology to generate adaptive strategies for games. Based on a set of low-level heuristics (or strategies), a hyperheuristic game player can generate strategies which adapt to both the behavior of the co-players and the game dynamics. By using a simple heuristic selection mechanism, a number of existing heuristics for specialized games can be integrated into an automated game player. As examples, we develop hyperheuristic game players for three games: iterated prisoner's dilemma, repeated Goofspiel and the competitive traveling salesmen problem. The results demonstrate that a hyperheuristic game player outperforms the low-level heuristics, when used individually in game playing and it can generate adaptive strategies even if the low-level heuristics are deterministic. This methodology provides an efficient way to develop new strategies for games based on existing strategies.

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生成游戏自适应策略的超启发式方法

超启发式已经成功地应用于解决各种计算搜索问题。在本文中，我们研究了一种生成游戏自适应策略的超启发式方法。基于一组低级启发式（或策略），超启发式游戏玩家可以生成既适应合作玩家的行为又适应游戏动态的策略。通过使用简单的启发式选择机制，可以将许多现有的专门游戏的启发式方法集成到自动游戏机中。作为例子，我们为三个游戏开发了超启发式游戏玩家：迭代囚犯困境、重复Goofspiel和竞争性旅行推销员问题。结果表明，当在游戏中单独使用时，超启发式游戏玩家优于低级别启发式游戏玩家，并且即使低级别启发式是确定性的，它也可以生成自适应策略。这种方法为在现有策略的基础上开发新的游戏策略提供了一种有效的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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

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.