Assessing the Cognitive Abilities of Alternate Learning Classifier System Architectures

D. A. Gaines
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引用次数: 1

Abstract

Abstract : Since its inception in the 1960s, the Genetic Algorithm (GA) framework for solving complex problems has been simultaneously intensely studied and deployed. Despite wide-ranging practical successes in engineering, manufacturing, applied, and social science domains, developing GA-based systems has been more art than science. Consequently some researchers have attempted to build and test theories and models for robust GA design. Given this attention to "pure" Genetic Algorithm research and implementations, progress on a subsequent GA-based framework called Learning Classifier Systems (LCS) lay dormant until the late 1990s. Stalwarts in GA/LCS research have opined that to further advance the field and facilitate theory formation, a broad study of LCSs, particularly one that focuses on their cognitive aspects, is needed. I wish to contribute to this theory building effort by examining, using simulation modeling and analyses, how alternative LCS architectures learn to cope with other artificial entities in challenging, artificial environments created using variants of the Iterated Prisoners Dilemma (PD) Tournament setting.. The use of competing entities in this setting may be likened to a number of practical applications in which different agents must negotiate or compete with each other. One possible application is the use of computer-based agents in negotiations in a buying-selling situation. In such an environment, a buyer's agent must attempt to discern the seller's negotiation pattern, and then use this information to accomplish its objective. In this example, an LCS- based agent could be used in repeated encounters with the seller to improve its performance with regard to a measure of interest such as price, quantity or delivery time.
评估替代学习分类器系统架构的认知能力
摘要:遗传算法(GA)框架自20世纪60年代提出以来,在求解复杂问题的同时得到了广泛的研究和应用。尽管在工程、制造、应用和社会科学领域取得了广泛的实际成功,但开发基于遗传算法的系统更像是艺术而不是科学。因此,一些研究人员试图建立和测试鲁棒遗传算法设计的理论和模型。考虑到这种对“纯”遗传算法研究和实现的关注,随后基于遗传算法的框架(称为学习分类器系统(LCS))的进展一直处于休眠状态,直到20世纪90年代末。GA/LCS研究的中坚分子认为,为了进一步推进该领域并促进理论形成,需要对LCS进行广泛的研究,特别是关注其认知方面的研究。我希望通过研究,使用仿真建模和分析,在具有挑战性的人工环境中,使用迭代囚犯困境(PD)锦标赛设置的变体创建的替代LCS架构如何学习应对其他人工实体,从而为这一理论建设工作做出贡献。在这种情况下,竞争实体的使用可以比作许多实际应用,在这些应用中,不同的代理必须相互谈判或竞争。一个可能的应用是在买卖谈判中使用基于计算机的代理。在这样的环境下,买方代理人必须试图辨别卖方的谈判模式,然后利用这些信息来实现其目标。在本例中,基于LCS的代理可以在与卖方的多次接触中使用,以提高其在价格、数量或交货时间等利益度量方面的表现。
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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