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
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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.
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页码：666 / 671
页数：6
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