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摘要翻译：
基于Agent的建模(ABM)是洞察社会现象的有力范式。反弹道导弹很少被应用的一个领域是联盟组建。传统上，联盟的形成是用合作博弈论来建模的。本文提出了一种启发式算法，该算法可以嵌入到ABM中，允许Agent找到联盟。由此产生的联盟结构与合作博弈论的解决方法相当，特别是核心。由于寻找合作博弈论解决方案的计算复杂性，因此需要一种启发式方法，这将其应用限制在大约几十个代理之间。ABM范例提供了一个平台，在这个平台中，简单的规则和代理之间的交互可以产生宏观级别的效果，而不需要大量的计算需求。因此，它可以成为一个有效的手段，以逼近合作对策解的大量Agent。我们的启发式算法结合了基于agent的建模和合作博弈论来帮助寻找作为游戏核心解决方案成员的agent分区。我们的启发式算法的准确性可以通过将其结果与实际的核心解进行比较来确定。这种比较是通过开发一个实验来实现的，该实验使用了一个名为手套游戏的合作游戏的具体例子。手套游戏是一种交换经济游戏。寻找传统的合作博弈论解决方案是计算密集的，因为每个可能的划分必须与每个可能的联盟进行比较，以确定核心集；因此，我们的实验只考虑最多九个玩家的游戏。结果表明，对于我们实验中考虑的游戏，我们的启发式方法在90%以上的时间内获得了核心解。
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英文标题：
《Finding Core Members of Cooperative Games using Agent-Based Modeling》
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作者：
Daniele Vernon-Bido, Andrew J. Collins
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最新提交年份：
2020
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Multiagent Systems 多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Economics 经济学
二级分类：Theoretical Economics 理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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英文摘要：
Agent-based modeling (ABM) is a powerful paradigm to gain insight into social phenomena. One area that ABM has rarely been applied is coalition formation. Traditionally, coalition formation is modeled using cooperative game theory. In this paper, a heuristic algorithm is developed that can be embedded into an ABM to allow the agents to find coalition. The resultant coalition structures are comparable to those found by cooperative game theory solution approaches, specifically, the core. A heuristic approach is required due to the computational complexity of finding a cooperative game theory solution which limits its application to about only a score of agents. The ABM paradigm provides a platform in which simple rules and interactions between agents can produce a macro-level effect without the large computational requirements. As such, it can be an effective means for approximating cooperative game solutions for large numbers of agents. Our heuristic algorithm combines agent-based modeling and cooperative game theory to help find agent partitions that are members of a games' core solution. The accuracy of our heuristic algorithm can be determined by comparing its outcomes to the actual core solutions. This comparison achieved by developing an experiment that uses a specific example of a cooperative game called the glove game. The glove game is a type of exchange economy game. Finding the traditional cooperative game theory solutions is computationally intensive for large numbers of players because each possible partition must be compared to each possible coalition to determine the core set; hence our experiment only considers games of up to nine players. The results indicate that our heuristic approach achieves a core solution over 90% of the time for the games considered in our experiment.
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PDF链接：
https://arxiv.org/pdf/2009.00519
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