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The FORTE system is available via anonymous ftp. This system contains the following items:
Bradley L. Richards and Raymond J. Mooney
Machine Learning 19,2 (1995), pp. 95-131. 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.