Rule-based training of neural networks

摘要

Rule-based expert systems either develop out of the direct involvement of a concerned expert or through the enormous efforts of intermediaries called knowledge engineers. In either case, knowledge engineering tools are inadequate in many ways to support the complex problem of expert system building. This article describes a set of experiments with adaptive neural networks which explore two types of learning, deductive and inductive, in the context of a rule-based, deterministic parser of Natural Language. Rule-based processing of Language is an important and complex domain. Experiences gained in this domain generalize to other rule-based domains. We report on those experiences and draw some general conclusions that are relevant to knowledge engineering activities and maintenance of rule-based systems.