Statistical approach toward designing expert system

• Main Author: Hu, Zhiji
• Other Authors: Ball State University. Dept. of Mathematical Sciences.
• Format: Others
• Published: 2011
• Subjects: Expert systems (Computer science); Uncertainty (Information theory)
• Online Access: http://cardinalscholar.bsu.edu/handle/handle/183695; http://liblink.bsu.edu/uhtbin/catkey/539812

Inference under uncertainty plays a crucial role in expert system and receives growing attention from artificial intelligence experts, statisticians, and psychologists. In searching for new satisfactory ways to model inference under uncertainty, it will be necessary to combine the efforts of researchers from different areas. It is expected that with deep insight into this crucial problem, it will not only have enormous impact on development of AI and expert system, but also bring classical areas like statistics into a new stage. This research paper gives a precise synopsis of present work in the field and explores the mechanics of statistical inference to a new depth by combining efforts of computer scientists, statisticians, and psychologists. One important part of the paper is the comparison of different paradigms, including the difference between statistical and logical views. Special attentions, which need to be paid when combining various methods, are considered in the paper. Also, some examples and counterexamples will be given to illustrate the availability of individual model which describes human behavior. Finally, a new framework to deal with uncertainty is proposed, and future trends of uncertainty management are projected. === Department of Mathematical Sciences