Abstract

Abstract. The purpose of this article is to summarize the state-of-the-art of the expert systems research field. First, we introduce the basic notion of knowledge, and specifically of shallow knowledge, and deep knowledge. The first section of the document summarizes the history of the field. We analyze the differences between the first generation of expert systems, based primarily upon rulebased and frame-based representation of shallow knowledge. We mainly concentrate on the most important expert systems and their impact on the subsequent research. These are the traditional Mycin and Prospector expert systems, but also less famous ones such as General Problem Solver, Logic Theory Machine, and others. Finally, we present some modern expert systems and shells such as Gensym’s G2 and also some light-weight prolog based expert systems, usually based on deep knowledge of the domain. In the forth section, we compare various knowledge representation languages. We briefly describe each, present some inference techniques, and also discuss primary the upsides and downsides. For each, we finally present successful expert systems and shells using the language. As for shallow knowledge, we review mainly rule-based and framebased

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