Revising first-order logic theories from examples through stochastic local search

• Type: Article in International Conference Proceedings Book
• Date: 2008

Authors

Aline Paes (Author) Other; Gerson Zaverucha (Author) Other; Vitor Santos Costa (Author) FCUP

Conference proceedings International

Title: INDUCTIVE LOGIC PROGRAMMING

Pages: 200-210

Joint Conference of the 17th International Conference on Inductive Logic Programming/24th International Conference on Machine Learning

Corvallis, OR, JUN 19-21, 2007

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 8 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-005-5P4

DOI: 10.1007/978-3-540-78469-2_21

Abstract (EN): First-Order Theory Revision from Examples is the process of improving user-defined or automatically generated First-Order Logic (FOL) theories, given a set of examples. So far, the usefulness of Theory Revision systems has been limited by the cost of searching the huge search spaces they generate. This is a general difficulty when learning FOL theories but recent work showed that Stochastic Local Search (SLS) techniques may be effective, at least when learning FOL theories from scratch. Motivated by these results, we propose novel SLS based search strategies for First-Order Theory Revision from Examples. Experimental results show that introducing stochastic search significantly speeds up the runtime performance and improve accuracy.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11