基于 ADOPT 算法的终止和最优性的反例和修正

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Koji Noshiro , Koji Hasebe
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引用次数: 0

摘要

分布式约束优化问题(DCOP)是多代理协调问题的建模框架。异步分布式优化(ADOPT)是一种著名的完整 DCOP 算法,在过去的十年中,人们提出了它的许多变体。人们认为,基于 ADOPT 的算法具有终止和最优的关键特性,这两个特性分别保证了算法在有限时间内终止和获得最优解。本文提出了基于 ADOPT 算法的终止性和最优性的反例。这些反例分为三类,在我们分析的 ADOPT 及其八个变体中,每一种都至少存在一个。换句话说,这些算法可能不会终止或以次优解终止。此外,我们还发现,ADOPT 的有界误差近似算法也存在缺陷,它能使算法在保证解的质量在预定误差范围内的情况下更快地终止。此外,我们还提出了 ADOPT 的修正版,以避免现有算法的缺陷,并证明它具有终止和最优性的特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Counterexamples and amendments to the termination and optimality of ADOPT-based algorithms

A distributed constraint optimization problem (DCOP) is a framework to model multi-agent coordination problems. Asynchronous distributed optimization (ADOPT) is a well-known complete DCOP algorithm, and many of its variants have been proposed over the last decade. It is considered proven that ADOPT-based algorithms have the key properties of termination and optimality, which guarantee that the algorithms terminate in a finite time and obtain an optimal solution, respectively. In this paper, we present counterexamples to the termination and optimality of ADOPT-based algorithms. They are classified into three types, at least one of which exists in each of ADOPT and eight of its variants that we analyzed. In other words, the algorithms may potentially not terminate or terminate with a suboptimal solution. Furthermore, we show that the bounded-error approximation of ADOPT, which enables the algorithm to terminate faster with the quality of the solution guaranteed within a predefined error bound, also suffers from flaws. Additionally, we propose an amended version of ADOPT that avoids the flaws in existing algorithms and prove that it has the properties of termination and optimality.

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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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