A Kripke-Lewis semantics for belief update and belief revision

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2025-02-01 DOI:10.1016/j.artint.2024.104259
Giacomo Bonanno
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Abstract

We provide a new characterization of both belief update and belief revision in terms of a Kripke-Lewis semantics. We consider frames consisting of a set of states, a Kripke belief relation and a Lewis selection function. Adding a valuation to a frame yields a model. Given a model and a state, we identify the initial belief set K with the set of formulas that are believed at that state and we identify either the updated belief set Kϕ or the revised belief set Kϕ (prompted by the input represented by formula ϕ) as the set of formulas that are the consequent of conditionals that (1) are believed at that state and (2) have ϕ as antecedent. We show that this class of models characterizes both the Katsuno-Mendelzon (KM) belief update functions and the Alchourrón, Gärdenfors and Makinson (AGM) belief revision functions, in the following sense: (1) each model gives rise to a partial belief function that can be completed into a full KM/AGM update/revision function, and (2) for every KM/AGM update/revision function there is a model whose associated belief function coincides with it. The difference between update and revision can be reduced to two semantic properties that appear in a stronger form in revision relative to update, thus confirming the finding by Peppas et al. (1996) [30] that, “for a fixed theory K, revising K is much the same as updating K”. It is argued that the proposed semantic characterization brings into question the common interpretation of belief revision and update as change in beliefs in response to new information.
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信念更新与修正的Kripke-Lewis语义
本文提出了基于Kripke-Lewis语义的信念更新和信念修正的新表征。我们考虑由一组状态、一个Kripke信念关系和一个Lewis选择函数组成的框架。将估值添加到框架中生成模型。给定一个模型和一个状态,我们用在该状态下相信的一组公式来识别初始信念集K,我们将更新的信念集K φ或修改的信念集K φ(由公式φ表示的输入提示)识别为(1)在该状态下相信的条件的结果的公式集,(2)有ϕ作为先决条件。我们证明了这类模型既具有Katsuno-Mendelzon (KM)信念更新函数的特征,也具有Alchourrón、Gärdenfors和Makinson (AGM)信念修正函数的特征,在以下意义上:(1)每个模型产生一个可以完成为完整KM/AGM更新/修正函数的部分信念函数;(2)对于每个KM/AGM更新/修正函数,都有一个与其相关联的信念函数重合的模型。更新和修订之间的区别可以简化为两种语义属性,这两种语义属性在修订中相对于更新以更强的形式出现,从而证实了Peppas et al.(1996)[30]的发现,“对于一个固定的理论K,修订K与更新K大致相同”。本文认为,所提出的语义表征对信念修正和更新的常见解释提出了质疑,即信念在响应新信息时发生了变化。
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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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Editorial Board A simple yet effective self-debiasing framework for transformer models A Kripke-Lewis semantics for belief update and belief revision EMOA*: A framework for search-based multi-objective path planning Formal verification and synthesis of mechanisms for social choice
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