循环赛四种变体的策略引出:以Goofspiel为例

M. Dror, G. Kendall, A. Rapoport
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引用次数: 1

摘要

Goofspiel是一个简单的两人零和博弈,不存在已知的均衡策略。为了深入了解什么是获胜策略,我们进行了一次循环赛,要求参与者提供电脑程序,以便在有结转或没有结转的情况下玩游戏。这两种变体中的每一种都是在两个完全不同的目标函数下进行的,即最大化在所有对手中赢得的累积点数(就像Axelrod的锦标赛),以及最大化赢得任何给定回合的可能性。我们的研究结果表明,就游戏的复杂性及其目标函数而言,结果确实存在固有的差异,而且获胜策略表现出一定程度的复杂性、深度和平衡性,这是目前的适应性学习模型所无法捕捉到的。
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Elicitation of Strategies in Four Variants of a Round-Robin Tournament: The Case of Goofspiel
Goofspiel is a simple two-person zero-sum game for which there exist no known equilibrium strategies. To gain insight into what constitute winning strategies, we conducted a round-robin tournament in which participants were asked to provide computerized programs for playing the game with or without carryover. Each of these two variants was to be played under two quite different objective functions, namely, maximization of the cumulative number of points won across all opponents (as in Axelrod's tournament), and maximization of the probability of winning any given round. Our results show that there are, indeed, inherent differences in the results with respect to the complexity of the game and its objective function, and that winning strategies exhibit a level of sophistication, depth, and balance that are not captured by present models of adaptive learning.
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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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