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Abstract

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.

Comparação de Funções de Avaliação em Revisão de Teorias Probabilísticas de Primeira-Ordem

“…• e satisfaz um critério de minimalidade. [Towell and Shavlik, 1994], [Garcez and Zaverucha, 1999], [Ramachandran and Mooney, 1998], [Buntine, 1991] e [Wogulis and Pazzani, 1993], [Richards and Mooney, 1995], [Wrobel, 1996] mostram que sistemas de revisão de teorias, proposicionais e de primeira-ordem respectivamente, podem aprender teorias mais precisas e com menos dados do que sistemas puramente indutivos.…”

“…A princípio qualquer operador utilizado em aprendizado de máquina (primeira-ordem) pode ser usado em um sistema de revisão de teoria. Abaixo descrevemos alguns desses operadores os quais foram de£nidos em [Richards and Mooney, 1995] .…”