An operator-based approach to first-order theory revision
Bradley L. Richards · 1992
Knowledge acquisition is a difficult and time-consuming task, and as error-prone as any human activity. Thus, knowledge bases must be maintained, as errors and omissions are discovered. To address this task, recent learning systems have combined inductive and explanation-based techniques to produce a new class of systems performing theory revision. When errors are discovered in a knowledge base, theory revision allows automatic self-repair, eliminating the need to recall the knowledge engineer and domain expert. To date, theory revision systems have been limited to propositional domains. This thesis presents a system, FORTE (First-Order Revision of Theories from Examples), that performs theory revision in first-order domains. Moving to a first-order representation creates many new challenges, such as argument selection and recursion. But it also opens many new application areas, such as logic programming and qualitative modelling, that are beyond the reach of propositional systems. F scORTE 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 revision, first-order induction, and inverse resolution. F scORTE has been tested in a wide variety of domains. This thesis includes results from standard machine-learning domains, logic programming, qualitative model building, and grammar acquisition.