Abstract

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‐doman 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.