Knowledge acquisition for classification expert systems

• Authors: William J. Clancey
• Venue: ACM '84 Proceedings of the 1984 annual conference of the ACM on The fifth generation challenge
• Year: 1984

Expert systems are generally described by a mixture of terms that confuse implementation language with knowledge structure and the search process. This confusion makes it difficult to analyze new problems and to derive a set of knowledge engineering principles. A rigorous, logical description of expert systems reveals that a small set of terms and relations can be used to describe many rule-based expert systems. In particular, one common method for solving problems is by classification—heuristically relating data abstractions to a preenumerated network of solutions. This model can be used as a framework for knowledge acquisition, particularly in the early stages for organizing the expert's vocabulary and decomposing problems.