Knowledge acquisition for classification expert systems

Abstract

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.