Knowledge-based learning integrating acquisition and learning

Authors:
Bradley L. Whitehall;Robert E. Stepp;Stephen C.-Y. Lu
Affiliations:
Department of Computer Science, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL;Department of Computer Science, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL;Department of Mechanical and Industrial Engineering, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
IEA/AIE '90 Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2
Year:
1990

Citing 4
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Abstract

Empirical learning algorithms are hampered by their inability to use domain knowledge to guide the induction of new rules. This paper describes knowledge-based learning, an approach to learning that selects the examples and relevant attributes for an empirical algorithm. Knowledge-based learning can be used for developing rules for engineering expert systems. Engineers often have some rules for problem solving, but also many experiences (examples) that facilitate solving problems. Knowledge-based learning systems are able to use both forms of knowledge.