具有强约束和弱约束的抽象论证框架

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2024-08-20 DOI:10.1016/j.artint.2024.104205
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引用次数: 0

摘要

处理有争议的信息是多种应用环境中的一个重要问题。形式化论证可以对支持和反对某一主张的论据进行推理,从而决定结果。Dung 的抽象论证框架 (AF) 已成为基于论证的推理的核心形式主义。Dung 的框架之所以成功并广受欢迎,关键在于其简单性和表达性。完整性约束有助于以简洁、自然的方式表达领域知识,从而使建模任务变得简单,即使是那些难以在 AF 中编码的问题也不例外。在本文中,我们首先探讨了分别基于 Kleene 逻辑和 Lukasiewicz 逻辑的两种直观语义,它们适用于增强了(强)约束的 AF--由此产生的论证框架被称为约束 AF(CAF)。然后,我们提出了一种新的论证框架,称为弱约束 AF(WAF),它用弱约束增强了 CAF。直观地说,这些约束可以用来为通过 CAF 定义的问题找到 "最优 "解决方案。我们对 CAF 和 WAF 进行了详细的复杂性分析,结果表明,在大多数情况下,强约束并不会提高 AF 的表达能力,而在一些著名的论证语义下,弱约束会系统地提高 CAF(和 AF)的表达能力。
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Abstract argumentation frameworks with strong and weak constraints

Dealing with controversial information is an important issue in several application contexts. Formal argumentation enables reasoning on arguments for and against a claim to decide on an outcome. Dung's abstract Argumentation Framework (AF) has emerged as a central formalism in argument-based reasoning. Key aspects of the success and popularity of Dung's framework include its simplicity and expressiveness. Integrity constraints help to express domain knowledge in a compact and natural way, thus keeping easy the modeling task even for problems that otherwise would be hard to encode within an AF. In this paper, we first explore two intuitive semantics based on Kleene and Lukasiewicz logics, respectively, for AF augmented with (strong) constraints—the resulting argumentation framework is called Constrained AF (CAF). Then, we propose a new argumentation framework called Weak constrained AF (WAF) that enhances CAF with weak constraints. Intuitively, these constraints can be used to find “optimal” solutions to problems defined through CAF. We provide a detailed complexity analysis of CAF and WAF, showing that strong constraints do not increase the expressive power of AF in most cases, while weak constraints systematically increase the expressive power of CAF (and AF) under several well-known argumentation semantics.

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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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