Use of a fuzzy machine learning technique in the knowledge acquisition process
Acquiring the knowledge to support an expert system is one of the key activities in knowledge engineering. Knowledge acquisition (KA) is closely related to research in the machine learning field. Any machine learning acquires some knowledge, but not enough knowledge for building expert systems. The aim of this article is to present a new approach to machine learning which helps to acquire knowledge when building expert systems. This technique will acquire the more general knowledge that should be used for extending, updating and improving an incomplete and partially incorrect knowledge base (KB). The main claim of our approach is that the system will start with poor knowledge, provided by the expert or the organization to which he belongs. A machine learning technique will evolve it to an incomplete KB, which may be used for further interactions with the expert, that will incrementally extend and improve it until obtaining a complete KB (i.e., with complete inferential capabilities). (C) 2001 Elsevier Science B.V. All rights reserved.
machine learning knowledge acquisition genetic algorithms