Automated Refinement of First-Order Horn-Clause Domain Theories

Authors:
Bradley L. Richards;Raymond J. Mooney
Affiliations:
Fachhochschule Furtwangen, Gerwigstr. 15, 78120 Furtwangen, Germany. bradley@ai-lab.fh-furtwangen.de;Department of Computer Sciences, University of Texas, Austin, Texas, 78712. mooney@cs.utexas.edu
Venue:
Machine Learning
Year:
1995

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Abstract

Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. FORTE uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including prepositional theory refinement, first-order induction, and inverse resolution. FORTE is demonstrated in several domains, including logic programming and qualitative modelling.