The language of Contextual Attribute Logics – Introduction and survey

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2025-05-30 DOI:10.1016/j.ijar.2025.109487

Bernhard Ganter

引用次数: 0

Abstract

This article provides an introductory overview of Contextual Attribute Logic(s), a branch of Contextual Logic founded by Rudolf Wille. It presents a logic language that is based on mathematical logic but uses a different terminology to better serve the interpretative goal of Formal Concept Analysis.

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语境属性逻辑语言——介绍与综述

本文介绍了上下文属性逻辑，它是由Rudolf Wille创立的上下文逻辑的一个分支。它提出了一种基于数学逻辑的逻辑语言，但使用不同的术语来更好地服务于形式概念分析的解释目标。

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

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.