在迭代信念修正中表示状态

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

迭代式信念修正需要有关当前信念的信息。这些信息由称为 "哆嗦状态 "的数学结构表示。大多数文献都专注于如何修正 "哆嗦状态",而忽略了它可能会以指数形式增长。我们针对最常见的哆嗦状态存储方式研究了这一问题。所有四种方法都能存储每一种哆嗦状态,但有些方法的空间比其他方法小。尤其是显式表示法(对当前信念的枚举)更浪费空间。水平表示法(命题公式序列)和自然表示法(自然修订的历史)比它更简洁。词法表示法(词法修订史)甚至比它们更简洁。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Representing states in iterated belief revision

Iterated belief revision requires information about the current beliefs. This information is represented by mathematical structures called doxastic states. Most literature concentrates on how to revise a doxastic state and neglects that it may exponentially grow. This problem is studied for the most common ways of storing a doxastic state. All four of them are able to store every doxastic state, but some do it in less space than others. In particular, the explicit representation (an enumeration of the current beliefs) is the more wasteful on space. The level representation (a sequence of propositional formulae) and the natural representation (a history of natural revisions) are more succinct than it. The lexicographic representation (a history of lexicographic revision) is even more succinct than them.

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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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