Stability based on single-agent deviations in additively separable hedonic games

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2024-05-31 DOI:10.1016/j.artint.2024.104160
Felix Brandt , Martin Bullinger , Leo Tappe
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Abstract

Coalition formation is a central concern in multiagent systems. A common desideratum for coalition structures is stability, defined by the absence of beneficial deviations of single agents. Such deviations require an agent to improve her utility by joining another coalition. On top of that, the feasibility of deviations may also be restricted by demanding consent of agents in the welcoming and/or the abandoned coalition. While most of the literature focuses on deviations constrained by unanimous consent, we also study consent decided by majority vote and introduce two new stability notions that can be seen as local variants of another solution concept called popularity. We investigate stability in additively separable hedonic games by pinpointing boundaries to computational complexity depending on the type of consent and friend-oriented utility restrictions. The latter restrictions shed new light on well-studied classes of games based on the appreciation of friends or the aversion to enemies. Many of our positive results follow from a new combinatorial observation that we call the Deviation Lemma and that we leverage to prove the convergence of simple and natural single-agent dynamics under fairly general conditions. Our negative results, in particular, resolve the complexity of contractual Nash stability in additively separable hedonic games.

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基于可加可分对冲博弈中单代理偏差的稳定性
联盟的形成是多代理系统的核心问题。联盟结构的一个共同要求是稳定性,即单个代理不出现有益偏差。这种偏离要求代理通过加入另一个联盟来提高其效用。此外,偏离的可行性还可能受到限制,因为它需要得到欢迎联盟和/或放弃联盟中的代理的同意。虽然大多数文献都关注一致同意限制的偏离,但我们也研究了由多数票决定的同意,并引入了两个新的稳定性概念,这两个概念可以看作是另一个叫做 "受欢迎程度 "的解决方案概念的局部变体。我们根据同意的类型和以朋友为导向的效用限制,指出计算复杂性的边界,从而研究可加可分对冲博弈的稳定性。后一种限制为基于对朋友的欣赏或对敌人的厌恶而研究得很透彻的博弈类别带来了新的启示。我们的许多正面结果都来自于一个新的组合观察,我们称之为偏差谬误,我们利用它来证明在相当普遍的条件下简单而自然的单个代理动力学的收敛性。我们的负面结果尤其解决了可加可分对冲博弈中契约纳什稳定性的复杂性。
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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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