Automated Reenement of First-order Horn-clause Domain Theories 1

Knowledge acquisition is a diicult, 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 reenement. This paper presents a system, Forte (First-Order Revision of Theories from Examples), which reenes rst-order Horn-clause theories by integrating a variety of diierent revision techniques into a coherent whole. Forte uses these techniques within a hill-climbing framework, guided by a global heuristic. It identiies 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 reene-ment, rst-order induction, and inverse resolution. Forte is demonstrated in several domains, including logic programming and qualitative modelling.