The Competitions of Forgiving Strategies in the Iterated Prisoner's Dilemma

Ruchdee Binmad, Mingchu Li, Nakema Deonauth, Theerawat Hungsapruek, Aree Limwudhikraijirath
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Abstract

The iterated prisoner's dilemma or IPD game has been widely used in modelling interactions among autonomous agents. According to the tournament competitions organized by Axelrod, Tit-for-Tat emerged as the most effective strategy on the assumption of an environment clinically free of communicative error or noiseless. However, with noise present, Tit-for- Tat contradictorily finds itself more difficult to maintain cooperation. In this study, the competitions of our proposed strategies and other Tit-for- Tat like strategies in the environment with different levels of noise are presented. The main result is that our proposed strategies provide the most effective performance in both round-robin tournaments and evolutionary dynamics.
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迭代囚徒困境中宽恕策略的竞争
迭代囚徒困境或IPD博弈已被广泛应用于自治主体之间相互作用的建模。根据阿克塞尔罗德组织的比赛,在假定临床环境没有沟通错误或无噪音的情况下,以牙还牙成为最有效的策略。然而,随着噪音的存在,针锋相对的矛盾发现自己更难以维持合作。在本研究中,我们提出的策略和其他针锋相对的策略在不同噪音水平的环境中的竞争。主要结果是,我们提出的策略在循环赛和进化动态中都提供了最有效的性能。
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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Proceedings: 2018 IEEE International Conference on Agents (ICA) Identifying safety properties guaranteed in changed environment at runtime A Cyclical Social Learning Strategy for Robust Convention Emergence Copyright Efficient Task Allocation with Communication Delay Based on Reciprocal Teams
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