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Aprendizado de Programas em Lógica Utilizando Redes Neurais

Rodrigo BasilioGerson ZaveruchaValmir Carneiro Barbosa

First-order theory refinement using neural networks is still an o pen problem. Towards a solution to this problem, we define a First-Order ext ensio n of the Cascade ARTMAP (FOCA) system, using Inductive Logic Pro gramming techniques. We: a) modify the network structure to handle first -o rder objects; b) define first-order versions of the choice (similarity) funct io n and of the vigilance criterion, the main functions that guide all Cascade ARTMAP dynamics; c) define a first-order version of the pro po sit io nal learning algorithm, that approximates Plotkin's least general generalizat io n (lgg). Results show that our initial goal, learning logic pro grams using neural networks, has been achieved. Clique no link abaixo para buscar o texto completo deste trabalho na Web: Buscar na Web

Biblioteca Digital Brasileira de Computação - Contato: bdbcomp@lbd.dcc.ufmg.br
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