具有紧凑表示的修正算子

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2024-02-02 DOI:10.1016/j.artint.2024.104080
Pavlos Peppas , Mary-Anne Williams , Grigoris Antoniou
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引用次数: 0

摘要

尽管 "信念修正 "领域在理论上取得了巨大进步,但在实现方面取得的成功却很有限。实现修正算子的障碍之一是其规范(更不用说计算)需要大量资源。另一方面,实现一个特定的修正算子(如 Dalal 的算子)的作用也很有限。在本文中,我们概括了 Dalal 的构造,定义了一整套具体的修正算子,称为参数化差分修正算子或简称 PD 算子。这个系列的范围很广,足以涵盖各种不同的应用,同时也很容易表示。除了语义定义之外,我们还从公理上描述了 PD 运算符族的特征(包括专门针对 Dalal 运算符的特征描述),证明它符合 Parikh 的相关性敏感公设 (P),研究了它的计算复杂性,并讨论了它对信念修正实现的益处。
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Revision operators with compact representations

Despite the great theoretical advancements in the area of Belief Revision, there has been limited success in terms of implementations. One of the hurdles in implementing revision operators is that their specification (let alone their computation), requires substantial resources. On the other hand, implementing a specific revision operator, like Dalal's operator, would be of limited use. In this paper we generalise Dalal's construction, defining a whole family of concrete revision operators, called Parametrised Difference revision operators or PD operators for short. This family is wide enough to cover a wide range of different applications, and at the same time it is easy to represent. In addition to its semantic definition, we characterise the family of PD operators axiomatically (including a characterisation specifically for Dalal's operator), we prove its' compliance with Parikh's relevance-sensitive postulate (P), we study its computational complexity, and discuss its benefits for belief revision implementations.

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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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