Possible low-priced, robust expert systems using neural networks and minimal entropy coding

摘要

Within this paper the following research results are presented. A combination of minimal entropy coding and neural network technique is proposed for low-priced and robust expert system building. A “thesis of tractability” describing the size and complexity of ordinary human expertise when using the above-mentioned methods in knowledge engineering is given. Developmental and maintenance costs of expert and decision support systems can be cut down by more than 90 percent below the costs encountered when rule-based shells are used. A PC-based expert system shell (named ZEUS) incorporating the above-mentioned principles has been developed, and two expert systems have been made. Both cases are tractable and significant cost reduction has been verified in specific cases. Principles, techniques, and functionalities of the program ZEUS are described and evaluated.