Extended theory refinement in knowledge-based neural networks

被引：0

作者：

Garcez, ASD ^{[1
]}

机构：

[1] Univ London Imperial Coll Sci Technol & Med, Dept Comp, London SW7 2BZ, England

来源：

PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年

关键词：

hybrid Architectures; extended logic programming; feedforward neural networks;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper shows that single hidden layer networks with semi-linear activation function compute the answer set semantics of extended logic programs. As a result, incomplete (nonmonotonic) theories, presented as extended logic programs, i.e. possibly containing both classical and default negation, may be refined through Inductive learning in knowledge-based neural networks.

引用

页码：2905 / 2910

页数：4

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