CIM — The Hybrid Symbolic/Connectionist Rule-Based Inference System

Abstract

Previous research has shown that connectionist models are suitable for cognitive and natural language processing tasks. An inference mechanism is a key element in commonsense reasoning in a natural language understanding system. This research project offers a connectionist alternative to Buchheit’s symbolic inference module for INFANT called the Connectionist Inference Mechanism (CIM). CIM is a hybrid cognitive model that combines the advantages of the symbolic approach, local representation, and parallel distributed processing. Moreover, it makes good use of its modular structure. Several modules work together in CIM, including memory, neural networks, and a binding set, to perform the inference generation. Besides rule application capability, CIM is also able to perform variable binding. A number of experiments have shown that CIM can make inferences appropriately.