Abstract
To solve the Imperfect Theory Problem (ITP) faced by Explanation Based Generalization (EBG), this paper proposes a methodology, Deep Knowledge Based Learning Methodology (DKBLM) by name, and gives an implementation of DKBLM, called Hierarchically Distributed Learning System (HDLS). As an example of HDLS's application, this paper shows a learning system (MLS) in meteorology domain and its running with a simplified example. DKBLM can acquire experiential knowledge with causality in it. It is applicable to those kinds of domains, in which experiments are relatively difficult to carry out, and in which there exist many available knowledge systems at different levels for the same domain (such as weather forecasting).
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