This chapter introduces the basics of neural-symbolic systems used thoughout the book. A
brief bibliographical review is also presented. Neural-symbolic systems have become a very …

A semantic network is a graph of the structure of meaning. This article introduces semantic
network systems and their importance in Artificial Intelligence, followed by I. the early …

This paper presents the Connectionist Inductive Learning and Logic Programming System
(C-IL 2 P). C-IL 2 P is a new massively parallel computational model based on a feedforward …

The paper presents a connectionist framework that is capable of representing and learning
propositional knowledge. An extended version of propositional calculus is developed and is …

A variety of ideas, approaches, and techniques exist--in terms of both architecture and
learning--and this abundance seems to lead to many exciting possibilities in terms of …

We define a notion of reasoning using world-rank-functions, independently of any symbolic
language. We then show that every symmetric neural network (like Hopfield networks or …

Synaptic weights for neurons in logic programming can be calculated either by using
Hebbian learning or by Wan Abdullah's method. In other words, Hebbian learning for …

Logic program and neural networks are two important perspectives in artificial intelligence.
Logic describes connections among propositions. Moreover, logic must have descriptive …

Symmetric networks designed for energy minimization such as Boltzman machines and
Hopfield nets are frequently investigated for use in optimization, constraint satisfaction and …

The second published collection based on a conference sponsored by the Metroplex
Institute for Neural Dynamics--the first is Motivation, Emotion, and Goal Direction in Neural …