REPRESENTING AND PROCESSING PRODUCTION SYSTEMS IN CONNECTIONIST ARCHITECTURES

Abstract
Much effort has been expended on developing special architectures dedicated to the efficient execution of problems in artificial intelligence (AI), especially production systems. While artificial neural networks (ANNs) offer the promise of solving various problems in pattern recognition and classification, we demonstrate here that the ANN approach can be applied to the AI production system paradigm. Among various types of neural networks, the three-layers of ring-structured feedback network is considered in this paper to suit the problem domain under investigation. Characteristics of the production system paradigm are identified. Various aspects of the use of feedback neural networks in mapping production systems are discussed. Two types of representation techniques are studied: local and hierarchical representations. A hierarchical representation derives features from patterns in production systems and constructs a 3-dimensional space called feature space, where a pattern can be uniquely defined by a vector. To demonstrate the efficient use of the neural network approach, a mapping of the generic production system is detailed throughout the paper. The results of a deterministic simulation demonstrate that the three layers of ring-structured feedback neural network architecture can be an efficient processing mechanism for the AI production system paradigm.

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