構造化多層ネットワークによるニューロエキスパートシステム

抄録

This paper proposes a framework for implementing expert systems based on a connectionist model, referred as to the neuro-expert. The conventional model of this framework is the connectionist symbol processing using distributed and recursive network models for knowledge representation. However, those models have problems in devising effective ways of representing complex knowledge structure. Our approach uses structured multi-layered models to represent the factual knowledge and rules. We propose a methodology of transforming the rules of the disjunctive normal forms. Each rule is transformed into the conjunctive normal form. Each transformed rule comprises a training example. The network architecture of the neuro-expert is predetermined from the structure of the transformed training examples. The architecture of the neuro-expert consists of several network modules with two-layers. Each network module is trained by the new learning algorithm, flash learning, that requires a single presentation of the training set. We will show the neuro-expert with the structured multi-layered networks has ability to represent complex knowledge structures with a simple inference mechanism. We also show a case study to examine the capability of the neuro-expert.