Granular Computing and Its Applications in Information Retrieval Support Systems

粒度计算探索思维、问题解决和信息处理中的多个粒度级别。 它使用由一系列综合级别组成的分层粒度结构。 一个级别由相同粒度或性质的相互连接和相互作用的颗粒组成。 粒是观察中使用的焦点或在解决问题时讨论中使用的单位，给出局部和部分描述。级别中的颗粒共同提供全局和完整的描述。 一系列粒状结构提供了同一问题的多个视图。 我的研究的一个中心论题是一个包含了哲学基础、方法论基础和计算范式三个方面的粒计算的三元理论。 它强调从多个视图和每个视图中的多个抽象级别对现实世界问题的结构化理解和解决方法。 拟议的研究计划旨在通过制定和阐明粒度计算的想法，充分发展三位一体的理论，并充分利用它们在人类问题解决和智能系统领域。通过研究和综合现有的研究成果，我们计划设计一种语言来描述精确的多级粒度结构，制定一套通用的学科无关的原则和机制，理解问题解决使用粒度结构，并构建和编译一套粒度计算算法，包括自顶向下，自底向上，中间出和渐进式计算。 对人类解决问题的基本原则的理解可以用来指导和授权更多的人更好地解决问题。 同样的原则也可以应用于设计具有人类智能的智能系统。 具体的模型，如粗糙集，形式概念分析和知识空间，将仔细研究。 原型信息检索支持系统将通过探索单个文档的粒度结构、文档空间、用户空间和领域知识空间来实现。

Granular computing explores multiple levels of granularity in thinking, problem solving, and information processing. It uses a hierarchical granular structure consisting of a family of integrative levels. A level consists of inter-connected and inter-acting granules of the same granularity or nature. A granule is a focal point used in an observation or a unit used in a discussion during problem solving, giving a local and partial description. The granules in a level collectively provide a global and complete description. A family of granular structures provides multiple views of the same problem. A central thesis of my research is a triarchic theory of granular computing encompassing the three perspectives of philosophical foundations, methodological foundations, and computational paradigms. It stresses a structured understanding of, and structured methods for solving, a real-world problem from multiple views and at multiple levels of abstraction within each view. The proposed research program aims to fully develop the triarchic theory by formulating and articulating granular computing ideas and to fully capitalize on them in the areas of human problem solving and intelligent systems. By examining and synthesizing results from existing studies, we plan to design a language for describing precisely multilevel granular structures, to formulate a set of general discipline-independent principles and mechanisms for understanding problem solving using granular structures, and to construct and compile a set of granular computing algorithms, including top-down, bottom-up, middle-out and progressive computing. An understanding of the underlying granule-based principles of human problem solving can be used to instruct and empower more people to be better at solving problems. The same principles can also be applied in designing intelligent systems with human-level intelligence. Concrete models, such as rough sets, formal concept analysis, and knowledge spaces, will be carefully investigated. Prototype information retrieval support systems will be implemented by exploring granular structures of individual documents, a document space, a user space, and a domain knowledge space.