Toward rough set based insightful reasoning in intelligent systems

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2025-03-12 DOI:10.1016/j.ins.2025.122078
Andrzej Skowron , Jaroslaw Stepaniuk
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Abstract

This paper explores a rough set-based approach for supporting insightful reasoning in Intelligent Systems (ISs). The novelty lies in the introduction of a new concept for approximate reasoning processes based on granular computations. Although many rough set theory extensions developed over time focus on reasoning about (partial) set inclusion, these approximation spaces sometimes fall short when dealing with crucial aspects of approximate reasoning within ISs. Specifically, these systems aim to construct high-quality approximations of compound decision granules that represent solutions. Here, we present the basis for insightful reasoning realized through approximate reasoning processes grounded in granular computations. By doing so, we provide a sufficiently rich basis for designing IS problem solvers. This basis allows ISs to restructure or adapt their reasoning based on the generated granular computations, ultimately leading to high-quality granular solutions.
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本文探讨了一种基于粗糙集的方法,用于支持智能系统(IS)中的深入推理。其新颖之处在于引入了基于粒度计算的近似推理过程的新概念。虽然随着时间的推移,许多粗糙集理论扩展都集中在(部分)集合包含的推理上,但这些近似空间在处理 IS 中近似推理的关键方面时有时会出现不足。具体来说,这些系统旨在构建代表解决方案的复合决策颗粒的高质量近似。在这里,我们介绍了通过以粒度计算为基础的近似推理过程实现深入推理的基础。通过这样做,我们为设计 IS 问题求解器提供了足够丰富的基础。在此基础上,信息系统可以根据生成的粒度计算调整或重组其推理过程,最终获得高质量的粒度解决方案。
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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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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