A Fully Connectionist Model Generator for Covered First-Order Logic Programs

被引：0

作者：

Bader, Sebastian ^{[1
]}

Hitzler, Pascal ^{[2
]}

Hoelldobler, Steffen ^{[1
]}

Witzel, Andreas ^{[3
]}

机构：

[1] Tech Univ Dresden, Int Ctr Computat Log, Dresden, Germany

[2] Univ Karlsruhe, AIFB, Karlsruhe, Germany

[3] Univ Amsterdam, Inst Log Language & Computat, Amsterdam, Netherlands

来源：

20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2007年

关键词：

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：

We present a fully connectionist system for the learning of first-order logic programs and the generation of corresponding models: Given a program and a set of training examples, we embed the associated semantic operator into a feed-forward network and train the network using the examples. This results in the learning of first-order knowledge while damaged or noisy data is handled gracefully.

引用

页码：666 / 671

页数：6

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