SC-net: A hybrid connectionist, symbolic system

Abstract

This paper describes the SC-net system that has been developed to provide expert systems capability augmented with learning in a hybrid connectionist/symbolic approach. A distributed connectionist representation of cells connected by links is used to represent symbolic knowledge. Rules may be directly encoded in the connectionist network or learned from examples. The learning method is a form of instance-based learning in which some of the individual instances in the training set are encoded by adding structure to the network and others cause modifications to biases in the network. Both continuous and nominal attributes are directly represented in the network structure. A limited form of variables in the form of attribute value bindings on the right-hand side of rules is supported. Relational comparators in the form of cell groups are also supported. Relational comparators and attribute value structures are represented by groups of connected cells in the network. The learning algorithm is presented and methods for providing generalization in an instance-based connectionist environment are presented. Empirical results are presented, which include learning in domains (fevers and gems) that contain uncertainty and the well-known iris, and soybean data sets together with a real world domain for semiconductor wafer fault diagnosis. The generalization ability of the learned network is shown to be good in several domains including iris. The system is shown to compare favorably with a nonneural instance-based learning algorithm IBL.