系统梳理软集理论及其扩展，揭示其在不确定性建模中的应用潜力。
A Dynamic Survey of Soft Set Theory and Its Extensions
- 以参数化方式建模不确定性，将每个属性映射到全集子集
- 涵盖超软集、双极软集、动态软集等10余种扩展形式
- 适合对不确定推理、决策支持感兴趣的读者
软集理论通过将每个属性（参数）映射到给定全集的子集，为参数化决策建模提供直接框架，从而结构化地表示不确定性。过去几十年中，该理论发展出多种变体，包括超软集、超超软集、树软集、双极软集和动态软集，并与拓扑学、拟阵理论等多个领域建立联系。本书以综述形式呈现软集理论及其主要扩展，重点介绍核心定义、代表性构造及当前研究的主要方向。
原文摘要 · Abstract (English)
Soft set theory provides a direct framework for parameterized decision modeling by assigning to each attribute (parameter) a subset of a given universe, thereby representing uncertainty in a structured way [1, 2]. Over the past decades, the theory has expanded into numerous variants-including hypersoft sets, superhypersoft sets, TreeSoft sets, bipolar soft sets, and dynamic soft sets-and has been connected to diverse areas such as topology and matroid theory. In this book, we present a survey-style overview of soft sets and their major extensions, highlighting core definitions, representative constructions, and key directions of current development.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。