Experience-based deductive learning

Author

Choi, Joongmin ; Shapiro, Stuart C.

Author_Institution

Dept. of Comput. Sci., State Univ. of New York, Buffalo, NY, USA

fYear

1991

fDate

10-13 Nov 1991

Firstpage

502

Lastpage

503

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

Keywords

inference mechanisms; knowledge representation; learning systems; deductive inference; deductive learning; knowledge base; knowledge migration; knowledge shadowing; problem solving; rule redundancies; specificity relationships; Computer science; Control systems; Engines; Learning systems; Problem-solving; Shadow mapping; Virtual reality;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools for Artificial Intelligence, 1991. TAI '91., Third International Conference on

Conference_Location

San Jose, CA

Print_ISBN

0-8186-2300-4

Type

conf

DOI

10.1109/TAI.1991.167033

Filename

167033