Expert Systems are definitely one of the major success of Artificial Intelligence. Unfortunately, the knowledge acquisition bottleneck hampers the development of Expert Systems in practical domains. Machine Learning is a promising solution to this problem and this paper discusses an Automatic Knowledge Acquisition and Refinement System(AKARS-1) for knowledge in rule form. Two major components AKARS-1, the learning component (AKA-1) and the Heuristic Refinement System (HERES) are detailed. A prototype of AKA-1 has been implemented and three case studies are used to validate this prototype. A comparison between ID3 and AKA-1 is presented. HERES employs heuristics and statistical methods to guide its search for refinements. These methods are also discussed in the paper.
|Title of host publication||Knowledge acquisition for knowledge-based systems|
|Editors||H. Motoda, R. Mizoguchi, J. Boose, B. Gaines|
|Number of pages||14|
|Publication status||Published - 1991|
WONG, M. L., & LEUNG, K. S. (1991). AKARS-1 : an Automatic Knowledge Acquisition and Refinement System. In H. Motoda, R. Mizoguchi, J. Boose, & B. Gaines (Eds.), Knowledge acquisition for knowledge-based systems (pp. 161-174). IOS Press.