Conventional expert systems are "brittle" in the sense that they require substantial human intervention to compensate for even slight variations in descriptions, and break easily when they reachthe edge of their knowledge. In response to this problem, this paper describes a prototype of a new generation of expert systems, called an adaptive expert system (AES), which is capable of adapting its knowledge dynamically and analogically. AES combines the focussed power of expert systems with the analogical problem solving abilities of case-based reasoning systems, and demonstrates much higher "IQs" than the expert systems currently available on the market.
CITATION STYLE
Zhou, H. H. (1990). Adaptive expert systems and analogical problem solving. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 468 LNCS, pp. 314–323). Springer Verlag. https://doi.org/10.1007/3-540-53504-7_89
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