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Expert reasoning combines voluminous domain-specific knowledge with more general factual and strategic knowledge. Whereas expert system builders have recognized the need for specificity and problem-solving researchers the need for generality, few attempts have been made to develop expert reasoning engines combining different kinds of knowledge at different levels of generality. This paper reports on the FERMI project, a computer-implemented expert reasoner in the natural sciences that encodes factual and strategic knowledge in separate semantic hierarchies. The principled decomposition of knowledge according to type and level of specificity yields both power and cross-domain generality, as demonstrated in FERMI's ability to apply the same principles of invariance and decomposition to solve problems in fluid statics. DC-circuits, and centroid location. Hierarchical knowledge representation and problem-solving principles are discussed, and illustrative problem solving traces are presented. (Author)

Expert reasoning in the natural sciences appears to make extensive use of a relatively small number of general principles and reasoning strategies, each associated with a larger number of more specific inference patterns. Using a dual declarative hierarchy to represent strategic and factual knowledge, we analyze a framework for a robust scientific reasoning engine. It is argued that such an engine could provide the ability to reason from basic principles in the absence of directly applicable specific information, principled knowledge acquisition by using existing general patterns to structure new information, and congenial explanation and instruction in terms of general and familiar patterns of inference., Prepared in cooperation with California Univ., Berkeley.

During the Civil War, Williams served as a surgeon in the 13th Alabama Infantry, C.S.A.

During the Civil War, Williams served as a surgeon in the 13th Alabama Infantry, C.S.A.