Induction of Horn clauses: methods and the plausible generalization algorithm

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Abstract

We are considering the problem of induction of uncertainty-free descriptions for concepts when arbitrary background knowledge is available, to perform constructive induction, for instance. As an idealised context, we consider that descriptions and rules in the knowledge-base are in the form of definite (Horn) clauses. Using a recently developed model of generality for definite clauses, we argue that some induction techniques are inadequate for the problem. We propose a framework where induction is viewed as a process of model-directed discovery of consistent patterns (constraints and rules) in data, and describe a new algorithm, the Plausible Generalization Algorithm, that has been used to investigate the sub-problem of discovering rules. The algorithm raises a number of interesting questions: How can we identify irrelevance during the generalization process? How can our knowledgebase answer queries of the form “What do (objects) X and Y have in common that is relevant to (situation) S?”

Original languageEnglish
Pages (from-to)499-519
Number of pages21
JournalInternational Journal of Man-Machine Studies
Volume26
Issue number4
DOIs
Publication statusPublished - Apr 1987
Externally publishedYes

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