An Information Theoretic Approach to Modeling Neural Network Expert Systems

Abstract

In this paper we propose several novel techniques for mapping rule bases, such as are used in rule based expert systems, onto neural network architectures. Our objective in doing this is to achieve a system capable of incremental learning, and distributed probabilistic inference. Such a system would be capable of performing inference many orders of magnitude faster than current serial rule based expert systems, and hence be capable of true real time operation. In addition, the rule based formalism gives the system an explicit knowledge representation, unlike current neural models. We propose an information-theoretic approach to this problem, which really has two aspects: firstly learning the model and, secondly, performing inference using this model. We will show a clear pathway to implementing an expert system starting from raw data, via a learned rule-based model, to a neural network that performs distributed inference.

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