选择性可信度受限的信念更新

中文摘要 AI 辅助

信念更新关注智能体的信念因底层世界变化而产生的改变。标准的Katsuno-Mendelzon更新假设认知输入可从所有初始可能世界纳入，而可信度受限的信念更新则针对每个源世界，限制被视为可信或可达的后继世界。不过，现有可信度受限方法将认知输入视为不可分割的整体，无法表示复合认知输入中仅部分可实现的情况。本文提出选择性可信度受限的信念更新，在执行可信度受限转换前，会针对每个源世界将认知输入转换为较弱的代理。我们为该类更新算子提供语义和公理化刻画，随后确定两类表现良好的子类：一是一致性保持更新算子，要求当原始认知输入一致时，每个转换后的认知输入从其源世界来看是可信的；二是极大一致性保持更新算子，额外要求所选代理是原始认知输入可信后承中信息最丰富的。最后，我们证明所提框架具有通用性：可信度受限的信念更新可作为特例被恢复，而当移除可信度限制且转换函数取恒等函数时，会得到Katsuno-Mendelzon信念更新。这些结果表明，该框架提供了统一且表达力更强的信念更新说明，涵盖已有方法同时支持依赖源的选择性接受。

英文摘要

Belief update concerns changes in an agent's beliefs induced by changes in the underlying world. Standard Katsuno-Mendelzon update assumes that an epistemic input can be incorporated from every initially possible world, whereas credibility-limited belief update restricts, for each source world, the successor worlds regarded as credible or reachable. Nevertheless, existing credibility-limited approaches treat the epistemic input as an indivisible whole, and therefore cannot represent cases in which only part of a compound epistemic input can be realized. We introduce selective credibility-limited belief update, in which the epistemic input is transformed, relative to each source world, into a weaker proxy before the credibility-limited transition is performed. We provide semantic and axiomatic characterizations of the resulting class of update operators. We then identify two well-behaved sub-classes; namely, consistency-preserving update operators, which require every transformed epistemic input to be credible from its source world whenever the original epistemic input is consistent, and maximal consistency-preserving update operators, which additionally require the selected proxy to be maximally informative among the credible consequences of the original epistemic input. Finally, we establish the generality of the proposed framework by showing that credibility-limited belief update is recovered as a special case, while Katsuno--Mendelzon belief update emerges when credibility restrictions are removed and the transformation functions are taken to be identities. These results demonstrate that the framework provides a unified and strictly more expressive account of belief update, encompassing established approaches while supporting source-dependent selective acceptance.

发表机构

• University of the Peloponnese（伯罗奔尼撒大学）
• American University of the Middle East（中东美国大学）

机构由 AI 辅助整理，请以论文原文为准。