ISSN:
0885-6125
Keywords:
theory revision
;
knowledge refinement
;
inductive logic programming
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract Knowledge acquisition is a difficult, 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 refinement. This paper presents a system, forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. FORTE uses these techniques within a hill-climbing framework, guided by a global heuristic. 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 prepositional theory refinement, first-order induction, and inverse resolution. FORTE is demonstrated in several domains, including logic programming and qualitative modelling.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1022611224557
