资源竞争和动态环境下分布式决策的概述和挑战

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Claudia Szabo, Robin Baker, Glen Pearce, Eyoel Teffera, Anthony Perry
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引用次数: 0

摘要

了解分布式决策方法的优点和缺点,因为它们是为有争议和动态的环境开发和部署的,这对于确保在实践中最大限度地利用最新的进展至关重要。在本研究中,我们将重点从两个角度来研究决策算法的使用,即情境与态势感知(CSA)和基于CSA发现的决策。我们介绍了所需特征的分类,并分析了现有方法如何满足这些特征。我们的分析发现,对决策方法的评估需要成熟,以考虑诸如使用的网络带宽、容错性和鲁棒性等关键属性。大多数实验分析都集中在表明该方法有效,通常在小规模场景中,并且诸如运行时,网络带宽,大小权重和功率等属性被严重忽视。这些方法都没有考虑大的行动空间或稀疏的奖励。我们讨论了现有工作的权衡和挑战,并强调了研究机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Overview and Challenges of Distributed Decision Making in Resource Contested and Dynamic Environments
Understanding the advantages and disadvantages of distributed decision making approaches as they are developed for and deployed in contested and dynamic environments is critical to ensure that recent advancements are used in practice to their maximum potential. In this survey, we focus on the use of decision making algorithms in two pespectives, namely, context and situational awareness (CSA) and decision making based on findings from CSA. We introduce taxonomies of required characteristics and analyse how they are met by existing approaches. Our analysis finds that evaluation of decision making approaches needs to mature to consider critical attributes such as the used network bandwidth, fault tolerance, and robustness among others. The broad majority of experimental analyses focused on showing that the approach works, typically in a small scale scenario, and that attributes such as runtime, network bandwith, and size weight and power, were critically overlooked. None of the approaches consider large action spaces or sparse rewards. We discuss trade-offs and challenges of existing work and highlight research opportunities.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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