Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach

IF 1.4 2区数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Theory and Practice of Logic Programming Pub Date : 2023-07-01 DOI:10.1017/s1471068423000248

MICHAEL GELFOND, JORGE FANDINNO, EVGENII BALAI

引用次数: 0

Abstract

Abstract This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.

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在实际因果关系分析中包含背景知识:一种答案集规划方法

摘要本文提出了一种丰富的知识表示语言，旨在形式化因果知识。这种语言用于准确而直接地形式化来自实际因果关系文献的通用基准示例。给出了原因的定义，并用于分析与代表这些例子的动作序列相关的变化的实际原因。

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

Theory and Practice of Logic Programming 工程技术-计算机：理论方法

CiteScore

4.50

自引率

21.40%

发文量

审稿时长

>12 weeks

期刊介绍： Theory and Practice of Logic Programming emphasises both the theory and practice of logic programming. Logic programming applies to all areas of artificial intelligence and computer science and is fundamental to them. Among the topics covered are AI applications that use logic programming, logic programming methodologies, specification, analysis and verification of systems, inductive logic programming, multi-relational data mining, natural language processing, knowledge representation, non-monotonic reasoning, semantic web reasoning, databases, implementations and architectures and constraint logic programming.