Don was one of the pioneers of our field, whose early research built the foundation for the area that would later come to be labeled "knowledge based systems" (and still later "expert systems"). Don received a B.S. in Electrical Engineering from Iowa State University in 1958, and an M.S. in Electrical Engineering from the University of California, Berkeley in 1964. He then entered the Ph.D. program at Stanford's newly created Cotiputer Science Department. While at Berkeley he met a young professor named Ed Feigenbaum, and when Feigenbaum moved to Stanford in 1965 Don became Ed's first Ph.D. student. Ed recalls: "In mid-1965 the DENDRAL project began in earnest, and Don was its first (and at the time its only) Ph.D. student.

Of the twenty chapters in the first published book on AI, the 1963 Computers and Thought anthology by Feigenbaum and Feldman, six had been previously published as Rand research reports (Armer, 1962; Feigenbaum, 1961; Newell, Shaw & Simon, 1957, 1958; Newell & Simon, 1961a; Tonge, 1959). Much of this early work in AI was the result of the collaboration of two Rand employees, Allen Newell and Cliff Shaw, and a Rand consultant, Herbert Simon of the Carnegie Institute of Technology (later to become Carnegie-Mellon University). Beginning in the mid-1950s Newell, Shaw, and Simon's research on the logic theory machine, their chess playing program, and the general problem solver (GPS) defined much of the AIrelated research during the first decade of AI. Their work encompassed research areas that are still prominent subfields of artificial intelligence: symbolic processing, heuristic search, problem solving, planning, learning, theorem proving, knowledge representation, and cognitive modeling. It is important to note that this surge of AI activity at Rand did not take place in isolation.

Search 4. Methods for assigning credit and blame--obtaining a local Performance Standard

Since production systems (PS's) were first proposed by Post (1943) general computational mechanism, the methodology has seen a great deal of development and has been applied to a diverse collection of problems. Despite the wide scope of goals and perspectives demonstrated by the various systems, there appear to be many recurrent themes. We present an analysis and overview of those themes, as well as a conceptual framework by which many of the seemingly disparate efforts can be viewed, both in relation to each other and to other methodologies. Accordingly, we use the term production system in a broad sense and show how most systems that have used the term can be fit into the framework. The comparison to other methodologies is intended to provide a view of PS characteristics in a broader context, with primary reference to procedurally based techniques, but also with reference to more recent developments in programming and the organization of data and knowledge bases.

Don was one of the pioneers the checkers player, and Waterman's. of our field, whose early research built the foundation for the "His subsequent contributions to protocol analysis, to area that would later come to be labeled "knowledge based the technology of rule-based systems, and to the literature of systems" (and still later "expert systems"). Don received a B.S. in Electrical Engineering from With Don's work on production systems in his thesis, it Iowa State University in 1958, and an M.S. in Electrical was only natural that he should move to Carnegie-Mellon to Engineering from the University of California, Berkeley in work with Allen Newell after acquiring his Ph.D. in 1968. He then entered the Ph.D. program at Stanford's Al takes up the story from there: newly created Cotiputer Science Department. While at "Don came to CMU in Psychology, rather than Computer Berkeley he met a young professor named Ed Feigenbaum, Science. As with many people in AI, he had an abiding and when Feigenbaum moved to Stanford in 1965 Don became interest in understanding human cognition, although it always Ed's first Ph.D. student.

THE AI MAGAZINE Summer, 1986 55 building one of the first stored-program digital computers, AI also had its share of controversy, however, at Rand the JOHNNIAC (see Figure 1) (Gruenberger, 1968);l and elsewhere. Given its quick rise to popularity and its George Dantzig and his associates were inventing linear ambitious predictions (Simon & Newell, 1958), AI soon programming (Dantzig, 1963); Les Ford and Ray Fulkerson had its critics, and one of the most prominent, Hubert were developing techniques for network flow analysis Dreyfus, published his famous critique of AI (Dreyfus, (Ford & Fulkerson, 1962); Richard Bellman was developing 1965) while he was consulting at Rand. In addition, the his ideas on dynamic programming (Bellman, 1953); early promise of automatic machine translation of text Herman Kahn was advancing techniques for Monte Carlo from one language to another (the emphasis at Rand was simulation (Kahn, 1955); Lloyd Shapley was revolutionizing on translation from Russian to English) produced only game theory (Shapley, 1951-1960); Stephen Kleene was modest systems, and the goal of fully automated machine advancing our understanding of finite automata (Kleene, translation was abandoned in the early 1960s.