A guide to expert systems
Communications of the ACM
What size net gives valid generalization?
Neural Computation
A connectionist expert system that actually works
Advances in neural information processing systems 1
Empirical Learning as a Function of Concept Character
Machine Learning
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural network learning and expert systems
Neural network learning and expert systems
Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
Introduction to artificial intelligence and expert systems
Introduction to artificial intelligence and expert systems
Symbolic knowledge and neural networks: insertion, refinement and extraction
Symbolic knowledge and neural networks: insertion, refinement and extraction
Knowledge-based artificial neural networks
Artificial Intelligence
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Expert Systems: Design and Development
Expert Systems: Design and Development
Structuring Knowledge In Vague Domains
IEEE Transactions on Knowledge and Data Engineering
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Multi-inference with Multi-neurules
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Integrating Hybrid Rule-Based with Case-Based Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Neurules: Improving the Performance of Symbolic Rules
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Rule-based update methods for a hybrid rule base
Data & Knowledge Engineering
Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems
International Journal of Hybrid Intelligent Systems
An expert system using an extended AND-OR graph
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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Knowledge-based neural networks (KBNNs) can be used as expert system knowledge bases. This approach shifts the interests in using connectionist knowledge bases for inferencing in an interactive fashion and giving reasonable justifications for their conclusions. The primary goal of this article is to present a good inference and control mechanism for such knowledge bases. For this purpose, the article develops a stand alone inference engine that uses a connectionist knowledge base, seeks to reduce the amount of data requested in order to reach a conclusion, and explains how a particular conclusion was reached. The inference engine was evaluated on illustrative example applications. Results obtained demonstrate that in spite of its simplicity the presented technique is superior to other techniques over sparse input knowledge bases.