Intensions and extensions of granules: A two-component treatment

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Approximate Reasoning Pub Date : 2024-03-25 DOI:10.1016/j.ijar.2024.109182
Tamás Mihálydeák , Tamás Kádek , Dávid Nagy , Mihir K. Chakraborty
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Abstract

The concept of a granule (of knowledge) originated from Zadeh, where granules appeared as references to words (phrases) of a natural (or an artificial) language. According to Zadeh's program, “a granule is a collection of objects drawn together by similarity or functionality and considered therefore as a whole”. Pawlak's original theory of rough sets and its different generalizations have a common property: all systems rely on a given background knowledge represented by the system of base sets. Since the members of a base set have to be treated similarly, base sets can be considered as granules. The background knowledge has a conceptual structure, and it contains information that does not appear on the level of base granules, so such information cannot be taken into consideration in approximations. A new problem arises: is there any possibility of constructing a system modeling the background knowledge better? A two-component treatment can be a solution to this problem. After giving the formal language of granules involving the tools for approximations, a logical calculus containing approximation operators is introduced. Then, a two-component semantics (treating intensions and extensions of granule expressions) is defined. The authors show the connection between the logical calculus and the two-component semantics.

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颗粒的内涵和外延:双组分处理
颗粒(知识)的概念源于扎德,颗粒是指自然语言(或人工语言)中的单词(短语)。根据 Zadeh 的方案,"颗粒是由相似性或功能性吸引在一起的对象集合,因此被视为一个整体"。帕夫拉克最初的粗糙集理论及其不同的概括都有一个共同的特性:所有系统都依赖于基集系统所代表的给定背景知识。由于基集的成员必须得到类似的处理,因此基集可以被视为颗粒。背景知识具有概念结构,它包含的信息不会出现在基粒的层面上,因此在近似时不能考虑这些信息。一个新的问题出现了:是否有可能构建一个更好地模拟背景知识的系统?双组分处理方法可以解决这个问题。在给出涉及近似工具的颗粒形式语言之后,我们将介绍一种包含近似算子的逻辑微积分。然后,定义了一个双组分语义(处理颗粒表达式的意图和扩展)。作者展示了逻辑微积分与双组分语义之间的联系。
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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