FORTE


FORTE (First Order Revision of Theories from Examples) is a machine learning system for modifiying a first-order Horn-clause domain theory to fit a set of training examples. FORTE uses a hill-climbing approach to revise theories. It identifies possible errors in an input theory and calls on a library of operators to develop possible revisions. These operators are constructed from methods such as propositional theory refinement, first-order induction, and inversion resolution.

The FORTE system is available via anonymous ftp. This system contains the following items:

  1. Quintus Prolog source code for Forte.
  2. Various domain files.
  3. Sample data sets, including "family", "king-rook-king", and "insert-after".
Pointers to papers on FORTE can be found on our ILP and Theory Revision publication pages. Below is the standard reference (click on the open book image).

  • Refinement of First-Order Horn-Clause Domain Theories
    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.

    estlin@cs.utexas.edu