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Experience-based deductive learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

A method of deductive learning is proposed to control deductive inference. The goal is to improve problem solving time by experience, when that experience monotonically adds knowledge to the knowledge base. Accumulating and exploiting experience are done by the schemes of knowledge migration and knowledge shadowing. Knowledge migration generates specific (migrated) rules from general (migrating) rules and accumulates deduction experience represented by specificity relationships between migrating and migrated rules. Knowledge shadowing recognizes rule redundancies during a deduction and prunes deduction branches activated from redundant rules. Three principles for knowledge shadowing are suggested, depending on the details of deduction experience representation.

Original languageEnglish
Title of host publicationThird Int Conf Tools Artif Intell
PublisherPubl by IEEE
Pages502-503
Number of pages2
ISBN (Print)0818623004
StatePublished - 1992
EventThird International Conference on Tools for Artificial Intelligence - San Jose, CA, USA
Duration: Nov 5 1991Nov 8 1991

Publication series

NameThird Int Conf Tools Artif Intell

Conference

ConferenceThird International Conference on Tools for Artificial Intelligence
CitySan Jose, CA, USA
Period11/5/9111/8/91

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