Neural networks and structured knowledge: Knowledge representation and reasoning

被引：2

作者：

Kurfess, FJ ^{[1
]}

机构：

[1] New Jersey Inst Technol, Dept Comp & Informat Sci, Newark, NJ 07102 USA

来源：

APPLIED INTELLIGENCE | 1999年 / 11卷 / 01期

关键词：

neural networks; knowledge representation; structured knowledge reasoning; connectionism; symbol processing; hybrid systems;

D O I：

10.1023/A:1008327412259

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This collection of articles is the first of two parts of a special issue on "Neural Networks and Structured Knowledge." The contributions to the first part shed some light on the issues of knowledge representation and reasoning with neural networks. Their scope ranges from formal models for mapping discrete structures like graphs or logical formulae onto different types of neural networks, to the construction of practical systems for various types of reasoning. In the second part to follow, the emphasis will be on the extraction of knowledge from neural networks, and on applications of neural networks and structured knowledge to practical tasks.

引用

页码：5 / 13

页数：9

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