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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2008

Autores

Aline Paes (Autor) Outra; Gerson Zaverucha (Autor) Outra; Vitor Santos Costa (Autor) FCUP

Ata de Conferência Internacional

Título: INDUCTIVE LOGIC PROGRAMMING

Páginas: 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

Indexação

ISI Web of Knowledge - 4 Citações

Scopus - 8 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11