基于回归的Nash均衡排序遗传算法求解多参与者组合博弈论问题

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Abdullah Konak;Sadan Kulturel-Konak
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引用次数: 2

摘要

摘要针对非合作同时组合博弈论中难以枚举博弈参与者的所有决策选项的问题,提出了一种基于遗憾的适应度分配策略,用于进化算法寻找纳什均衡。进化算法在非合作同步博弈中的应用一直受到限制,因为在引导进化搜索到平衡点(通常是目标空间中的劣势点)方面存在挑战。本文提出了一种基于后悔的多种群遗传算法——基于后悔的纳什均衡排序遗传算法(RNESGA)来选择下一代参与者的候选决策选项。我们证明了RNESGA可以在单次运行中收敛到多个纳什均衡,使用两人和三人竞争背包游戏和其他来自文献的游戏。我们还表明,纯基于收益的适应度分配策略在三人博弈中表现不佳。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Regret-Based Nash Equilibrium Sorting Genetic Algorithm for Combinatorial Game Theory Problems with Multiple Players
We introduce a regret-based fitness assignment strategy for evolutionary algorithms to find Nash equilibria in noncooperative simultaneous combinatorial game theory problems where it is computationally intractable to enumerate all decision options of the players involved in the game. Applications of evolutionary algorithms to non-cooperative simultaneous games have been limited due to challenges in guiding the evolutionary search toward equilibria, which are usually inferior points in the objective space. We propose a regret-based approach to select candidate decision options of the players for the next generation in a multipopulation genetic algorithm called Regret-Based Nash Equilibrium Sorting Genetic Algorithm (RNESGA). We show that RNESGA can converge to multiple Nash equilibria in a single run using two- and three- player competitive knapsack games and other games from the literature. We also show that pure payoff-based fitness assignment strategies perform poorly in three-player games.
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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
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