Artificial intelligence (AI) is playing an increasingly larger role in production and manufacturing engineering. Much of this growth is the result of special-purpose computer controlled machines that are dominating modem manufacturing operations, such as computer numerically controlled machines and robots, and production activities, such as materials handling and process planning. Since a great deal of production and manufacturing engineering knowledge can be put in the form of rules, expert systems have emerged as a promising practical tool of AI in solving manufacturing and production engineering problems. The expert systems allow knowledge to be used for constructing human-machine systems that have specialized methods and techniques for solving problems in a particular application area. Over the years, many expert systems have been developed for applications in manufacturing and production engineering. Most of these expert systems, however, have been of little use to practitioners at large. The primary reason for this limited utility is that in most cases the developers do not divulge the knowledge base and inference mechanism that form the backbone of an expert system. Without the knowledge base, users can only derive a very limited benefit from an expert system and, for all practical purposes, a technical publication describing the expert system for the reader merely becomes a publicity brochure. The reader must either develop his own knowledge base or purchase the system from the developer, often at a substantial cost.