Knowledge acquisition is a difficult and time-consuming task, and as error-prone as any human activity. The task of automatically improving an existing knowledge base using learning methods is addressed by a new class of systems performing theory refinement. Until recently, such systems were limited to propositional theories. This paper presents a system, FORTE (First-Order Revision of Theories from Examples), for refining first-order Horn-clause theories. Moving to a first-order representation opens many new problem areas, such as logic program debugging and qualitative modelling, that are beyond the reach of propositional systems. FORTE uses a hill-climbing approach to revise theories. 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 propositional theory refinement, first-order induction, and inverse resolution. FORTE has been tested in several domains including logic programming and qualitative modelling.

PDF, PS

Machine Learning, Vol. 19, 2 (1995), pp. 95-131.

@Article{richards:mlj95, title={Automated Refinement of First-Order Horn-Clause Domain Theories}, author={Bradley L. Richards and Raymond J. Mooney}, volume={19}, journal={Machine Learning}, number={2}, key={FORTE}, pages={95-131}, url="http://www.cs.utexas.edu/users/ai-lab?richards:mlj95", year={1995} }