Multilayer feedforward networks are universal approximators
Neural Networks
Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Symmetric neural networks and propositional logic satisfiability
Neural Computation
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Foundations of Logic Programming
Foundations of Logic Programming
Neural-Symbolic Learning System: Foundations and Applications
Neural-Symbolic Learning System: Foundations and Applications
Approximating the Semantics of Logic Programs by Recurrent Neural Networks
Applied Intelligence
CHCL - A Connectionist Infernce System
Proceedings of the International Workshop on Parallelization in Inference Systems
Neural-symbolic intuitionistic reasoning
Design and application of hybrid intelligent systems
Connectionist model generation: A first-order approach
Neurocomputing
Connectionist weighted fuzzy logic programs
Neurocomputing
Logic Programs under Three-Valued Łukasiewicz Semantics
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Logics and Networks for Human Reasoning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Hi-index | 0.00 |
Knowledge based artificial networks networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes it is not obvious at all how neural symbolic systems should look like such that they are truly connectionist and allow for a declarative reading at the same time. The core method aims at such an integration. It is a method for connectionist model generation using recurrent networks with feed-forward core. After an introduction to the core method, this paper will focus on possible connectionist representations of structured objects and their use in structure-sensitive reasoning tasks.