文章基本信息

标题：Relevance in Belief Update
本地全文：下载
作者：Theofanis Aravanis
期刊名称：Journal of Artificial Intelligence Research
印刷版ISSN：1076-9757
出版年度：2021
卷号：72
页码：1-33
DOI：10.1613/jair.1.12772
语种：English
出版社：American Association of Artificial
摘要：It has been pointed out by Katsuno and Mendelzon that the so-called AGM revision operators defined by Alchourrón Gärdenfors and Makinson do not behave well in dynamically-changing applications. On that premise Katsuno and Mendelzon formally characterized a different type of belief-change operators typically referred to as KM update operators which to this date constitute a benchmark in belief update. In this article we show that there exist KM update operators that yield the same counter-intuitive results as any AGM revision operator. Against this non-satisfactory background we prove that a translation of Parikh’s relevance-sensitive axiom (P) in the realm of belief update suffices to block this liberal behaviour of KM update operators. It is shown both axiomatically and semantically that axiom (P) for belief update essentially encodes a type of relevance that acts at the possible-worlds level in the context of which each possible world is locally modified in the light of new information. Interestingly relevance at the possible-worlds level is shown to be equivalent to a form of relevance that acts at the sentential level by considering the building blocks of relevance to be the sentences of the language. Furthermore we concretely demonstrate that Parikh’s notion of relevance in belief update can be regarded as (at least a partial) solution to the frame ramification and qualification problems encountered in dynamically-changing worlds. Last but not least a whole new class of well-behaved relevance-sensitive KM update operators is introduced which generalize Forbus’ update operator and are perfectly-suited for real-world implementations.
关键词：belief revision and update;knowledge representation;reasoning about actions and change