Co-evolution of strategies for multi-objective games under postponed objective preferences

Erella Eisenstadt, A. Moshaiov, G. Avigad
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引用次数: 12

Abstract

The vast majority of studies that are related to game theory are on Single Objective Games (SOG), also known as single payoff games. Multi-Objective Games (MOGs), which are also termed as multi payoff, multi criteria or vector payoff games, have received lesser attention. Yet, in many practical problems, generally each player cope with multiple objectives that might be contradicting. In such problems, a vector of objective functions must be considered. The common approach to deal with MOGs is to assume that the preferences of the players are known. In such a case a utility function is used, which transforms the MOG into a surrogate SOG., This paper deals with non-cooperative MOGs in a non-traditional way. The zero-sum MOG, which is considered here, involves two players that postponed their objective preferences, allowing them to decide on their preferences after tradeoffs are revealed. To solve such problems we propose a co-evolutionary algorithm based on a worst-case domination relation among sets. The suggested algorithm is tested on a simple differential game (tug-of-war). The obtained results serve to illustrate the approach and demonstrate the applicability of the proposed co-evolutionary algorithm.
延迟目标偏好下多目标博弈策略的协同进化
绝大多数与博弈论相关的研究都是关于单目标游戏(SOG),也被称为单收益游戏。多目标游戏(mog),也被称为多收益、多标准或矢量收益游戏,很少受到关注。然而,在许多实际问题中,通常每个玩家都要应对多个可能相互矛盾的目标。在这类问题中,必须考虑目标函数向量。处理mog的常见方法是假设玩家的偏好是已知的。在这种情况下,使用一个实用函数,它将MOG转换为代理SOG。本文从非传统的角度对非合作mog进行了研究。这里考虑的是零和MOG,涉及到两个参与者推迟他们的客观偏好,允许他们在权衡之后决定他们的偏好。为了解决这类问题,我们提出了一种基于集合间最坏情况支配关系的协同进化算法。建议的算法在一个简单的微分游戏(拔河)上进行了测试。得到的结果有助于说明该方法并证明了所提出的协同进化算法的适用性。
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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