R1 is a rule-based program that configures VAX-11 computer systems. Given a customer's purchase order, it determines what, if any, substitutions and additions have to be made to the order to make it consistent and complete and produces a number of diagrams showing the spatial and logical relationships among the 90 or so components that typically constitute a system. The program has been used on a regular basis by Digital Equipment Corporation's manufacturing organization since January of 1980. R1 has sufficient knowledge of the configuration domain and of the percliarities of the various configuration constraints that at each step in the configuration process, it simply recognizes what to do; thus it requires little search in order to configure a computer system.

Abstract: It has been claimed that production systems have several advantages over other representational schemes. These include the potential for general self-augmentation (i.e., learning of new behavior) and the ability to function in complex environments. The production system language, OPS, was implemented to test these claims. In this paper we explore some of the issues that bear on the design of production system languages and try to show the adequacy of OPS for its intended purpose. I. INTRODUCTION Much of the work that has been done with production systems during the past few years has had as its primary goal the development of systems that are expert in some particular task. The tasks so far addressed include: chemical inference [Buchanan and Lederberg, J 971], medical diagnosis [Davis, Buchanan, and Shortliffe, 1975], discovery in mathematics [Lenat, 1976], speech recognition [Erman and Lesser, 1975; McCracken, 1977], and automatic programming [Barstow, 1977]. Although many of these systems have shown impressive power in the particular task for which they were designed, there remains a question of how suitable the production system representation is for large general problem solving programs. The Instructable Production System (IPS) project at CMU [Rychener and Newell, 1977] is attempting to answer this question. It has been claimed that production systems are capable of learning in a nontrivial way. If this is true, a production system should be able to learn not only facts, but also new behaviors.