Belief updating from integrity constraints and queries

作者:

摘要

It is argued that the problems of intensional knowledge base updating and incremental concept-learning—when formulated in a logical framework—can be understood as instances of the more general problem of belief updating. This insight allows interesting cross-fertilization between both areas. To support this claim, we sketch a simple extension of Shapiro's Model Inference System that solves the belief updating problem within a restricted subset of first order logic. This extension uses integrity constraints and allows for the assertion of non-unit clauses. The former generalizes the use of examples in concept-learning whereas the latter generalizes the set of revisions considered in knowledge base updating.

论文关键词:Concept-learning,knowledge base updating,integrity constraints,oracle,machine learning,inductive inference,data bases,knowledge bases,belief revision,belief updating

论文评审过程:Available online 19 February 2003.

论文官网地址:https://doi.org/10.1016/0004-3702(92)90075-9