A Recency Inference Engine for Connectionist Knowledge Bases

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Abstract

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.

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