Name
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. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1023/A:1022611224557 | Machine Learning |
Keywords | Field | DocType |
theory revision,knowledge refinement,inductive logic programming | Inductive logic programming,Heuristic,Horn clause,Computer science,Artificial intelligence,Operator (computer programming),Knowledge base,Logic programming,Knowledge acquisition,Machine learning,Inverse resolution | Journal |
Volume | Issue | ISSN |
19 | 2 | 1573-0565 |
Citations | PageRank | References |
91 | 5.33 | 31 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bradley L. Richards | 1 | 209 | 37.47 |
| Raymond J. Mooney | 2 | 10408 | 961.10 |