Author:

Keywords:

concept-learning, knowledge base updating, integrity constraints, oracle, machine learning, inductive inference, data bases, knowledge bases, belief revision, belief updating, databases, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, CONCEPT-LEARNING, KNOWLEDGE BASE UPDATING, INTEGRITY CONSTRAINTS, ORACLE, MACHINE LEARNING, INDUCTIVE INFERENCE, DATA BASES, KNOWLEDGE BASES, BELIEF REVISION, BELIEF UPDATING, DATABASES, 0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics, 1702 Cognitive Sciences, Artificial Intelligence & Image Processing, 4602 Artificial intelligence, 4603 Computer vision and multimedia computation, 4611 Machine learning

Abstract:

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.