Neural network learning and expert systems
Neural network learning 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
Knowledge-based neurocomputing
Knowledge-based neurocomputing
Hybrid Neural Network and Expert Systems
Hybrid Neural Network and Expert Systems
Connectionist-Symbolic Integration: From Unified to Hybrid Approaches
Connectionist-Symbolic Integration: From Unified to Hybrid Approaches
Neural-Symbolic Learning System: Foundations and Applications
Neural-Symbolic Learning System: Foundations and Applications
A Recency Inference Engine for Connectionist Knowledge Bases
Applied Intelligence
Rule Revision With Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Updating a Hybrid Rule Base with New Empirical Source Knowledge
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Combining symbolic and connectionist learning methods to refine certainty-factor rule-bases
Combining symbolic and connectionist learning methods to refine certainty-factor rule-bases
Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems
International Journal of Hybrid Intelligent Systems
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In this paper, we present methods for efficient updates of a hybrid rule base. The hybrid rule base consists of neurules, a type of hybrid rules combining symbolic rules and neural networks. A neurule base, called the target knowledge, is produced by conversion from a symbolic rule base, called its source knowledge. The presented methods concern modifications to the target knowledge, due to insertion of a new rule in or removal of an old rule from its source knowledge. The methods (a) require as little re-conversion as possible and (b) preserve the number of neurules as small as possible. This is achieved by storing information related to the conversion process in a tree, called the splitting tree. Experimental results demonstrate the benefits of using the splitting tree.