ALLEN NEWELL

March 19, 1927-July 19, 1992

BY HERBERT A. SIMON

WITH THE DEATH from cancer on July 19, 1992, of Allen Newell the field of artificial intelligence lost one of its premier scientists, who was at the forefront of the field from its first stirrings to the time of his death and whose research momentum had not shown the slightest diminution up to the premature end of his career. The history of his scientific work is partly my history also, during forty years of friendship and nearly twenty of collaboration, as well as the history of the late J. C. (Cliff) Shaw, a longtime colleague; but I will strive to make this account Allen-centric and not intrude myself too far into it. I hope I will be pardoned if I occasionally fail.1

If you asked Allen Newell what he was, he would say, “I am a scientist." He played that role almost every waking hour of every day of his adult life. How would he have answered the question, "What kind of scientist?" We humans have long been obsessed with four great questions: the nature of matter, the origins of the universe, the nature of life, the workings of mind. Allen Newell chose for his life's work answering the fourth of these questions. He was a person who not only dreamt but gave body to his dream, brought it to life. He had a vision of what human thinking



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--> ALLEN NEWELL March 19, 1927-July 19, 1992 BY HERBERT A. SIMON WITH THE DEATH from cancer on July 19, 1992, of Allen Newell the field of artificial intelligence lost one of its premier scientists, who was at the forefront of the field from its first stirrings to the time of his death and whose research momentum had not shown the slightest diminution up to the premature end of his career. The history of his scientific work is partly my history also, during forty years of friendship and nearly twenty of collaboration, as well as the history of the late J. C. (Cliff) Shaw, a longtime colleague; but I will strive to make this account Allen-centric and not intrude myself too far into it. I hope I will be pardoned if I occasionally fail.1 If you asked Allen Newell what he was, he would say, “I am a scientist." He played that role almost every waking hour of every day of his adult life. How would he have answered the question, "What kind of scientist?" We humans have long been obsessed with four great questions: the nature of matter, the origins of the universe, the nature of life, the workings of mind. Allen Newell chose for his life's work answering the fourth of these questions. He was a person who not only dreamt but gave body to his dream, brought it to life. He had a vision of what human thinking

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--> is. He spent his life enlarging that vision, shaping it, materializing it in a sequence of computer programs that exhibited the very intelligence they explained. THE CAREER In a remarkable talk about his research strategies and history given at Carnegie Mellon University in December 1991, seven months before his death,2 Allen described his career as aimed single-mindedly at understanding the human mind, but he also confessed to four or five substantial diversions from that goal—almost all of which produced major scientific products of their own. These "diversions" included his work with Gordon Bell on computer hardware architectures, the work with Stu Card and Tom Moran on the psychology of human-computer interaction, a major advisory role in the ARPA program of research on speech recognition, and his leadership in establishing computer science at Carnegie Mellon University and in creating the pioneering computer networking of that university's campus. For the rest, Allen's work aimed steadily, from the autumn of 1955 onward, at using computer simulation as the key research tool for understanding and modeling the human mind. After the first burst of activity, which produced the Logic Theorist, the General Problem Solver, and the NSS chess program, he focused increasingly on identifying and overcoming the limitations and inflexibilities of these models that impeded their extension into a wholly general theory of the mind. His final book, Unified Theories of Cognition (1990), records the vast progress that he and others made over thirty years toward such generality, progress that in the final decade of his life focused on the emerging Soar system that he and his colleagues built.

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--> HOW IT BEGAN Allen Newell was born in San Francisco on March 19, 1927, the son of Dr. Robert R. Newell, a distinguished professor of radiology at Stanford Medical School, and Jeanette Le Valley Newell. An older sister was his only sibling. His father provided an important model for his son. In an interview (McCorduck, 1979, p. 122), Allen once said of him: "He was in many respects a complete man.... He'd built a log cabin up in the mountains.... He could fish, pan for gold, the whole bit. At the same time, he was the complete intellectual.... Within the environment where I was raised, he was a great man. He was extremely idealistic. He used to write poetry." Allen's childhood was uneventful enough, many of the summers being spent in the Sierra Nevada at the log cabin his father built. Allen acquired a love of the mountains that never left him (an early ambition was to become a forest ranger) and a love of sports that, combined with his 6'1" height and sturdy build, led to the high school football team. He said of his own high school career (Newell, 1986, p. 347): "Allen was an indifferent pupil, though some people seemed to think he was bright. He went to Lowell High School-the intellectual high school of San Francisco-where he turned on academically. He also fell in love at age 16 with a fellow student, Noel McKenna, and married her as soon as tactically possible (age 20)." The marriage demonstrated that Allen and Noel were excellent decision makers even at that early age, for they formed a close and mutually supporting pair throughout the forty-five years of their marriage. Allen graduated from high school just as World War II was ending, worked for the summer in a shipyard, and then enlisted in the U.S. Navy. Although close to his father and

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--> acquainted with many other scientists who were family friends, he had no intention, up to that time, of following a scientific career. Adoption of science as his vocation came, he said, rather suddenly, when, serving on a U.S. Navy ship that carried scientific observers to the Bikini nuclear tests and assigned the task of making maps of the radiation distribution over the atolls, he was infected with the excitement of the scientific enterprise. On completing his service in the Navy, Allen enrolled in Stanford University, where he majored in physics. Undergraduate research led to his first paper, on X-ray optics (Newell and Baez, 1949). Stanford also exposed him in the classroom to George Polya, who was not only a distinguished mathematician but also a thoughtful student of mathematical discovery. Polya's widely read book, How to Solve It, published in 1945, had introduced many people (including me) to heuristic, the art of discovery. Allen came away from that experience aware that the processes of discovery could be investigated and analyzed and that heuristic-the art of guided search-played a key role in creative thinking. (Our common fascination with heuristic helps account for the rapidity with which Allen and I established common ground on first meeting early in 1952.) RAND A year in mathematics (1949-50) as a graduate student at Princeton and exposure to game theory, invented shortly before by von Neumann and Morgenstern, convinced Allen that he preferred a combination of experimental and theoretical research to pure mathematics. Taking a leave from Princeton, he found a position at the RAND Corporation, the then-new think tank in Santa Monica, in a group that was studying logistics problems of the Air Force. Two technical reports he coauthored with Joseph B. Kruskal (A Model

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--> for Organization Theory [1950] and Formulating Precise Concepts in Organization Theory [1951]) demonstrate his interest at that time in applying formal methods to complex empirical phenomena. Both papers adopt a style of axiomatization that was fashionable then in game theory and economics. A six-week field visit to the Munitions Board in Washington impressed Allen with the distance that separated the formal models from reality, and his trip report, Observations on the Science of Supply (1951), exhibits his sensitivity to and sophistication about the organizational realities that he observed (probably reinforcing his brief naval experience and summer's work in the wartime shipyard). Somewhat disillusioned with axiomatization as the route to reality, Allen then turned to the design and conduct of laboratory experiments on decision making in small groups, a topic of considerable active interest in RAND at that time. Dissatisfied also with small-group experiments as a way of studying organizations, the RAND team of John Kennedy, Bob Chapman, Bill Biel, and Allen conceived of constructing and operating a full-scale simulation of an Air Force Early Warning Station in order to study the organizational processes of the station crews. This effort, funded by the Air Force in 1952, led to the creation of the Systems Research Laboratory at RAND (eventually spun off as the Systems Development Corporation) (Chapman et al., 1959). Central to the research was recording and analyzing the crew's interactions with their radar screens, with interception aircraft, and with each other. These data focused Allen's attention on the information-handling and decision-making processes of the crew members and led to a search for an appropriate technique for analyzing and modeling the process. I met Allen when I became a consultant to the laboratory, and in the first minutes of our initial meeting

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--> he and I found common ground in the study of information processes as a route to understanding human decision making in organizations. One of Allen's special responsibilities in the project was to find a way to simulate a radar display of air traffic, for no technology was available to the lab for making appropriate simulated patterns of blips as they move over radar screens. While searching for computational alternatives, Allen met Cliff (. C.) Shaw, a RAND systems programmer, then working with the Card-Programmed Calculator, a prehistoric device that just preceded the first stored-program computers. Allen and Cliff conceived the idea of having the CPC calculate the successive air pictures and print out simulated radar maps. This not only provided the required laboratory simulation but also demonstrated to Al and Cliff (and to me when I learned of it) that computers, even prehistoric computers, could do more than arithmetic: they could produce spatial arrangements of nonnumerical symbols representing airplanes. Now two of the preconditions were in place for Allen's move to the goal of understanding human thinking. He clearly saw information processing as a central activity in organizations, and he had had a first experience in symbolic computing. A third precondition derived from contact with the stored-program computer Johnniac that John von Neumann was building at RAND in about 1954. At this time the ideas of cybernetics and artificial life were abroad. W. Ross Ashby had published in 1952 his Design for a Brain. W. Grey Walter (1953) in England had constructed some mechanical "turtles" that wandered about the room searching for a wall outlet when their batteries ran low, and similar creatures were built by Merrill Flood's group at RAND. By 1950 both Turing and Shannon had described (but not actually programmed) strategies for com-

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--> puter chess, and in 1952 I described (but did not implement) a program extending Shannon's ideas. On an auto trip en route to observing some Air Force exercises in the summer of 1954, Allen and I discussed at length the possibilities of using a computer to simulate human problem solving, but we were not then diverted from our current research on organizations. THE COMMITMENT In September 1954 Allen attended a seminar at RAND in which Oliver Selfridge of Lincoln Laboratories described a running computer program that learned to recognize letters and other patterns. While listening to Selfridge characterizing his rather primitive but operative system, Allen experienced what he always referred to as his "conversion experience." It became instantly clear to him "that intelligent adaptive systems could be built that were far more complex than anything yet done." To the knowledge Allen already had about computers (including their symbolic capabilities), about heuristic, about information processing in organizations, about cybernetics, and proposals for chess programs was now added a concrete demonstration of the feasibility of computer simulation of complex processes. Right then he committed himself to understanding human learning and thinking by simulating it. The student of organizations became a student of the mind. In the months immediately following Selfridge's visit Allen wrote (1955) "The Chess Machine: An Example of Dealing with a Complex Task by Adaptation," which outlined an imaginative design for a computer program to play chess in humanoid fashion, incorporating notions of goals, aspiration levels for terminating search, satisfying with "good enough" moves, multidimensional evaluation functions, the generation of subgoals to implement goals, and something

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--> like best first search. Information about the board was to be expressed symbolically in a language resembling the predicate calculus. The design was never implemented, but ideas were later borrowed from it for use in the NSS chess program in 1958. Newell's goals already extended far beyond chess: "The aim of this effort, then, is to program a current computer to learn to play good chess. This is the means to understanding more about the kinds of computers, mechanisms, and programs that are necessary to handle ultracomplicated problems (Newell, 1955). When the paper was presented in March 1955 at the Western Joint Computer Conference, Walter Pitts, the commentator for the session, said, "But, whereas [the authors of the other papers] are imitating the nervous system, Mr. Newell prefers to imitate the hierarchy of final causes traditionally called the mind. It will come to the same thing in the end, no doubt. .. ." From the very beginning something like the physical symbol system hypothesis was embedded in the research. THE LOGIC THEORIST AND LIST PROCESSING Even before his "conversion" Allen had been making plans to move to Pittsburgh early in 1955, with Noel and their new son Paul, to work with me in organizational research and earn a doctoral degree (in industrial management!). RAND agreed to continue to employ Allen as its (one-man) Pittsburgh outpost. This plan was duly executed but with the crucial alteration that the research was to be on programming a chess machine. It was arranged that Cliff Shaw at RAND would collaborate with us, and the program would run on RAND's Johnniac. For various technical and accidental reasons chess soon changed to geometry and geometry to logic, and the Logic Theory Machine (LTM), which discovered proofs for theorems in the propositional calcu-

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--> lus, emerged as a hand simulation by December 15, 1955, and a running program in the summer of 1956. Work was pursued simultaneously on a programming language that would be adequate for implementing the design, leading to the invention of the Information Processing Languages (IPLs), the first list-processing languages for computers. It is fair to say that the LTM and its successor, the General Problem Solver, laid the foundation for most of the artificial intelligence programs of the following decade. A genuine computer program performing a task of some sophistication has much more persuasive and educational powers than do verbal discussions of ideas. A running program is the moment of truth. LTM was not a "deduction machine"—in fact, it worked backwards, inductively, from hypothesized theorem to the axioms. Discovering proofs is much like discovering anything else, a process of selective search. The fact that the task involves symbolic logic does not make the problem solving process any more "logical" or less "intuitive" than if some other task (e.g., looking for a law that would connect the distances of planets from the sun with their periods of revolution) were in question. Although this work was incorporated in Allen's doctoral dissertation, I never regarded him as my "student." Allen, Cliff, and I were research partners, each contributing his knowledge to a wholly joint product. Allen, when he arrived in Pittsburgh, already had five years of scientific work under his belt and needed colleagues more than teachers. I do not suggest that he did not learn-he never stopped growing and learning throughout his life-but he learned as scientists learn, from everyone and everything around them, especially from observation of nature itself. Why did this particular work, which was part of an already existing Zeitgeist that had engaged the efforts of many

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--> able scientists, become highly visible and influential? An essential element in its impact was the actual running program. In addition, LTM and its successors were not directed at a single task. The specific programs were steps toward the solution of the general problem: understanding the human mind. The strategy is stated clearly in the first publication on LTM: "In this paper we describe a complex information processing system ... capable of discovering proofs for theorems in symbolic logic. This system, in contrast to the systematic algorithms . . . ordinarily employed in computation, relies heavily on heuristic methods similar to those that have been observed in human problem solving activity. The specification is written in a formal language, of the nature of a pseudo-code . . . for digital computers .... The logic theory machine is part of a program of research to understand complex information processing systems by specifying and synthesizing a substantial variety of such systems for empirical study" (Newell and Simon, 1956).3 It is all there: complex information processing, symbolic computation, heuristic methods, human problem solving, a programming language, empirical exploration. These are the components of the fundamental research strategy of the Carnegie-RAND group in 1955 and 1956 that continued to guide Allen Newell's scientific work throughout his career. It led him continually to identify and diagnose the limitations of the programs he built and to ponder about architectures that would remove those limitations, and it led him in the last decade to Soar-not as the final answer, for he knew that there are no final answers in science, but as the next step of progress along a path that he followed as long as he was able to work. EXPLOITING THE FIRST SUCCESS For about five years after 1955 the Newell-Shaw-Simon

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--> team, aided by a growing circle of graduate students, pushed forward the research ideas opened up by LTM and the IPL programming languages. Among the main thrusts in which Allen was involved were thinking-aloud protocols, the General Problem Solver, information-processing languages and production systems, the NSS chess-playing program, and human problem solving. Until 1961 he remained on the staff of RAND (in Pittsburgh); in that year he accepted appointment as an institute professor at Carnegie Institute of Technology. THINKING-ALOUD PROTOCOLS There are severe difficulties in testing a theory of human thinking that predicts the sequence of thought processes each of only a few hundred milliseconds duration. Apart from neurological evidence, which is only now beginning to become available for tracing some processes, there were few obvious ways of obtaining data while a task was being performed, even at a density of one data point per second. It occurred to the team to instruct subjects to think aloud while performing problem-solving tasks. However, fifty years earlier the method called “introspection" had been thoroughly discredited as a means of obtaining reliable data in psychology. Hence, it was necessary to show that the thinking-aloud method was quite different from classical introspection and to determine the circumstances under which it could provide objective evidence about thought processes. A program of laboratory experimentation using thinking-aloud methods was launched by the beginning of 1957; formal methods were developed for encoding protocol data (problem behavior graphs); and a decade later Allen and Don Waterman made the first, only partially successful, attempt at automating protocol analysis (Waterman and Newell, 1971).

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--> THE CAMPUS NETWORK From about 1972 the experience of members of the computer science department with the ARPA network convinced the community that a network of electronic communications was essential not only for the department but for the university. With the department having persuaded the university administration that Carnegie Mellon was in a unique position to offer national leadership in this direction, Allen agreed to serve as chairman of a task force that was appointed to prepare a plan and to educate the campus community about its potential. In February 1982 the task force issued its report, The Future of Computing at Carnegie Mellon University. An agreement was reached with IBM for collaboration in designing and installing the system, and the Andrew system, CMU's campus-wide network—one of the first in the nation—came into being. (The Andrews were Andrew Carnegie and Andrew Mellon.) ARPA From its beginnings artificial intelligence and simulation of human thinking have been foci of controversy, eliciting disbelief and anger from those who find the idea of a machine thinking either incredible or threatening. Decisions about funding AI research inevitably became enmeshed in this controversy about its worth, and the support by ARPA of computer science in general and AI in particular was periodically under attack throughout a long and stormy history. A very large slice of Allen's life was spent preparing research proposals and budget defenses for computer science at Carnegie Mellon, as well as participating in ARPA planning exercises and interpreting AI and cognitive science research to the broader scientific community. This, too, is a

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--> normal part of institution building in science, but not its pleasantest part. Allen, while resenting the time lost in these duties, never shirked them. However, his belief (and mine) was that propaganda of the deed was more important than propaganda of the word: that in the longer run the fate of AI and cognitive simulation would be determined not by debates with philosophers about what was possible, a priori, but by our success or failure in building programs that demonstrably simulate and thereby provide theoretical explanations for human thought processes. Every possible waking moment was to be reserved for that task. COGNITIVE SCIENCE AND AAAI Professional organizations are important among the institutions of science, and Allen played his role in them also. It was an honor that he was proud of, but no surprise, that he was elected the first president of the American Association for Artificial Intelligence and received the first Award for Research Excellence from the International Joint Conference on Artificial Intelligence. Editorships, however, were not for him. ALLEN, THE PERSON Allen Newell was a memorable person in the most literal meaning of that phrase. I will draw here on my own impressions as recorded in my autobiography (Simon, 1991) and follow these with some comments by others who knew him well. When I first met Al at RAND in 1952, he was 25 years old and fully qualified for tenure at any university—full of imagination and technique... His energy was prodigious, he was completely dedicated to science, and he had an unerring instinct for important (and difficult) problems. If these remarks suggest that he was not only bright but brash, they are not misleading.

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--> If imagination and technique make a scientist, we must also add dollars. I learned . . . [from Al] . . . how to position the decimal point in a research proposal.... Thinking big has characterized Al's whole research career, not thinking big for bigness' sake, but thinking as big as the task invites... From our earliest collaborations, Al has kept atrocious working hours. By this I . . . mean . . . that he works at the wrong time of day .... He preferred sessions that began at eight in the evening and stretched almost to dawn. I would have done most of my day's work by ten that morning, and by ten in the evening was ready to sleep, and not always able not to. Perhaps his greatest pleasure . . . is an “emergency" that requires him to stay up all night or two consecutive nights. I recall his euphoria on our visit to March Air Force Base in 1954, when the air exercise extended over a whole weekend, twenty-four hours per day. Some of these memories are frivolous, but high spirits, good humor, and hard work have characterized my relations with Al from the beginning. Allen was serious but not solemn. Whimsy and laughter came easily and often to him. Life, sometimes perplexing, was not a plodding march but a vivid drama in which he acted with brilliance and eclat, quite aware of the dramatic effects he was producing. This too was obvious early on. The Systems Research Laboratory operated on the grandest scale, its cast an entire Air Force unit. Only Allen and his codirectors could have dreamed up theater on this megabuck scale at a time when behavioral scientists might timidly request $5,000 or $10,000 for their research. His forceful qualities and his exuberance impressed themselves on all who met him. As Cliff Shaw recalled (McCorduck, 1979): "Energy is the thing I remember mainly about working with Al. Energy and brilliance. Long phone calls and long sessions on the teletype were typical. We would have sessions late into the night at Al's home. I felt like I was tagging along behind, trying to get that Johnniac to do what we already knew

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--> could be done. And with Al's energy, it was a good thing he had IPL-V, the programming language, as another outlet, so all that energy didn't descend on me." And much later, from a graduate student: "Allen Newell was not my adviser ... and was not on my thesis committee. But still Allen shaped my thinking: from him I learned every day more and more what research is. Through him I understood how to work dynamically towards my research goals.... Very rapidly, the initial intimidation I felt ... was transformed into admiration, friendship and respect." That testimonial, given at his memorial service, could be duplicated dozens of times over by his co-workers-from full professors to new graduate students. In his work with students he was patient, his criticism was constructive, he never lost his temper. If he had any faults as a mentor (and who does not?), it was probably in becoming so involved himself in his students' research problems that he sometimes provided them with more structure and more insights than was good for them. They had to work very hard and fast to retain the strategic initiative. Everything he attended to he attended to with energy and depth-whether it was his current research problem or an inquiry directed to him by a student or a visitor. In fact, it was this inability to address matters superficially that made the diversions weigh so heavily on Allen, taking him from his main research for considerable periods of time. But he handled the diversions with the same cheery enthusiasm and eclat as he did the mainstream tasks. It is hard to recall a lackluster Newell performance, whether it be a public address, a conversation in his office, or the analysis of a thinking-aloud protocol. I have said nothing about Allen's family life or leisure. Noel and Allen with their son Paul formed a close-knit family. As much of his work was done on the computer at home,

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--> he was not at all an absentee husband or father, but shared his activities with his family in spite of his marathon work week. The Newells enjoyed entertaining their friends, most of them from CMU or other academic communities. The categories of introvert or extrovert don't quite seem to fit Allen; he worked long hours in his study, but he spent enormous amounts of time with other people—usually engaged in professional tasks. There was little evidence of, or time for, the simpler kinds of leisure or hobbies unrelated to his work. He traveled fairly often abroad, usually with Noel, but mostly to professional meetings, and only occasionally did he add more than a few vacation days to these trips. At the very end of his life I learned—much to my astonishment, as I had had no inkling of it—that he frequently watched Sunday afternoon (or was it Saturday afternoon?) TV football games, perhaps a bit of fond nostalgia for his high school athletic days. It is fitting to conclude this account with a selection from Allen Newell's own set of maxims for the dedicated scientist, proposed in his "Desires and Diversions" talk of December 1991, for these maxims describe his own life: To each scientific life, its own style; and each style defines a life. Science is in the details. To work with the results of field X, you must be a professional in X. There is no substitute for working hard—very hard. A scientist is a transducer from nature to theory; seek out nature and listen to her. The scientific problem chooses you; you don't choose it. New things get their start by evolution or change, not design. Everything must wait until its time; science is the art of the possible. Diversions occur, make them count; salvage what is possible for the main goal. Solve whatever problems must be solved; but do not be seduced by them.

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--> Deep scientific ideas are exceedingly simple; others usually see them as trivial. and, finally: Choose a final project to outlast you. For Allen, Soar was that project. NOTES 1.   In preparing this account of Allen Newell's life I have drawn heavily on a briefer memorial (Simon, 1993) published in Artificial Intelligence and on a more complete one published by John Laird and Paul Rosenbloom (1992) in AI Magazine. Newell's papers are deposited in the Archives of Hunt Library at Carnegie Mellon University, where can also be found the transcripts of lengthy interviews with Newell by Pamela McCorduck, which were used extensively in her Machines Who Think (1979), and by Arthur L. Norberg, who interviewed Newell about his activities in connection with ARPA. 2.   This talk was videotaped and is available by writing to University Video Communications, P.O. Box 5129, Stanford CA 94309. 3.   Although, for reasons that are no longer obvious, Cliff Shaw was not a coauthor of this paper; he was a full partner in the entire research effort. REFERENCES Ashby, W. R. 1952. Design for a Brain. New York: Wiley. Bell, C. G., and A. Newell. 1971. Computer Structures: Readings and Examples. New York: McGraw-Hill. Bell, C. G., W. Broadley, W. Wulf, A. Newell, C. Pierson, R. Reddy, and S. Rege. 1971. C.mmp: The CMU Multiminiprocessor Computer: Requirements, Overview of the Structure, Performance, Cost and Schedule. Technical Report, Computer Science Department, Carnegie Mellon University, Pittsburgh. Berkeley, E. C. 1949. Giant Brains, or Machines That Think. New York: Wiley. Bowden, B. V., ed. 1953. Faster Than Thought. New York: Pitman. (Contains Turing's description of a chess-playing program.) Card, S., T. P. Moran, and A. Newell. 1983. The Psychology of Human-Computer Interaction. Hillsdale, N.J.: Erlbaum.

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--> Chapman, R. L., J. L. Kennedy, A. Newell, and W. C. Biel. 1959. The systems research laboratory's air defense experiments. Manage. Sci. 5:250-69. Freeman, P., and A. Newell. 1971. A model for functional reasoning in design. In Proceedings of the Second International Joint Conference on Artificial Intelligence. The British Computer Society, London, England, pp. 621-40. Kruskal, J. B., Jr., and A. Newell. 1950. A Model for Organization Theory. Technical Report LOGS-103. Santa Monica, Calif.: RAND Corporation. Laird, J., and A. Newell. 1983. A Universal Weak Method. Technical report, Computer Science Department, Carnegie Mellon University, Pittsburgh. Laird, J., and P. Rosenbloom. 1992. In pursuit of mind: The research of Allen Newell. AIMag. 13(4):17-45. McCorduck, P. 1979. Machines Who Think. San Francisco: W. H. Freeman. Medress, M. F., F. S. Cooper, J. W. Forgie, C. C. Green, D. H. Klatt, M. H. O'Malley, E. P. Newburg, A. Newell, D. R. Reddy, B. Ritea, J. E. Shoup-Hummel, D. E. Walker, and W. A. Woods. 1977. Speech understanding systems: A report of a steering committee. SIGART Newslett. 62:4-8. Moore, J., and A. Newell. 1974. How Can Merlin Understand? In Knowledge and Cognition, ed. L. Gregg. Hillsdale, N.J.: Erlbaum. Newell, A. 1951. Observations on the Science of Supply. Technical Report D-926. Santa Monica, Calif.: RAND Corporation. Newell, A. 1955. The chess machine: An example of dealing with a complex task by adaptation. In Proceedings of the 1955 Western Joint Computer Conference. Institute of Radio Engineers, New York, pp. 101-108. (Also issued as RAND Technical Report P-620.) Newell, A., ed. 1961. Information Processing Language VManual Englewood Cliffs, N.J.: Prentice-Hall. Newell, A. 1962. Some problems of basic organization in problem solving programs. In Self Organizing Systems, eds. M. C. Yovits, G. T. Jacobi, and G. D. Goldstein. Washington, D.C.: Spartan. Newell, A. 1963. Learning, generality and problem solving. In Proceedings of the IFIP Congress-62, pp. 407-12. Newell, A. 1965. Limitations of the current stock of ideas for prob-

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--> lem solving. In Conference on Electronic Information Handling, eds., A. Kent and O. Taulbee. Washington, D.C.: Spartan. Newell, A. 1966. On the representation of problems. Comput. Sci. Res. Rev., pp. 45-58. Newell, A. 1968. The trip towards flexibility. In Bio-engineering-An Engineering View, ed. G. Bugliarello. San Francisco: San Francisco Press. Newell, A. 1972. A theoretical exploration of mechanisms for coding the stimulus. In Coding Processes in Human Memory, eds. A. W. Melton and E. Martin. Washington, D.C.: Winston. Newell, A. 1973. Production systems: Models of control structures. In Visual Information Processing, ed. W. C. Chase. New York: Academic Press. Newell, A. 1982. The knowledge level. Artif. Intell. 18:87-127. Newell, A. 1986. Awards for distinguished scientific contributions: 1985 . Am. Psychol. 41:347-53. Newell, A. 1990. Unified Theories of Cognition. Cambridge, Mass.: Harvard University Press. Newell, A. 1992. Unified theories of cognition and the role of Soar. In Soar: A Cognitive Architecture in Perspective, eds. J. A. Michon and A. Anureyk. Dordrecht: Kluwer Academic Publishers. Newell, A., and A. V. Baez. 1949. Caustic curves by geometric construction. Am. Phys. 29:45-47. Newell, A., and G. Ernst. 1965. The search for generality. In Proceedings of FIP Congress 65:195-208. Newell, A., and J. B. Kruskal, Jr. 1951. Formulating Precise Concepts in Organization Theory. Technical Report RM-619-PR. Santa Monica, Calif.: RAND Corporation. Newell, A., and H. A. Simon. 1956. The logic theory machine: A complex information processing system. IRE Trans. Inf. Theory IT2:61-79. Newell, A., and H. A. Simon. 1972. Human Problem Solving. Englewood Cliffs, NJ.: Prentice-Hall. Newell, A., and H. A. Simon. 1976. Computer science as empirical inquiry: Symbols and search. Commun. Assoc. Comput. Machinery 19:111-26. Newell, A., J. C. Shaw, and H. A. Simon. 1958. Chess-playing programs and the problem of complexity. IBMJ. Res. Develop. 2:320-25.

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--> Newell, A., J. C. Shaw, and H. A. Simon. 1960. Report on a general problem solving program. In Proceedings of the International Conference on Information Processing. UNESCO, Paris, pp. 256-64. Newell, A., D. McCracken, G. Robertson, and R. Akscyn. 1982. ZOG and the USS Carl Vinson. Comput. Sci. Res. Rev. (Computer Science Department, Carnegie Mellon University, Pittsburgh.) Newell, A.,J. Barnett,J. Forgie, C. Green, D. Klatt,J. C. R. Licklider, M. Munson, R. Reddy, and W. Wood. 1971. Speech Understanding Systems: Final Report of a Study Group. Department of Computer Science, Carnegie Mellon University, Pittsburgh . Newell, A., F. S. Cooper, J. W. Forgie, C. C. Green, D. H. Klatt, M. F. Medress, E. P. Neuberg, M. H. O'Malley, D. R. Reddy, B. Ritea, J. E. Shoup, D. E. Walker, and W. A. Woods. 1975. Considerations for a Follow-on ARPA Research Program for Speech Understanding Systems. Technical report, Computer Science Department, Carnegie Mellon University, Pittsburgh. Polya, G. 1945. How to Solve It. Princeton, N.J.: Princeton University Press. Polya, G. 1954. Mathematics and Plausible Reasoning. Princeton, N.J.: Princeton University Press. Rosenbloom, P. S., and A. Newell. 1982. Learning by chunking: Summary of a task and a model. In Proceedings of AAAI-82 National Conference on Artificial Intelligence. AAAI, Menlo Park, Calif. Shannon, C. E. 1950. Programming a digital computer for playing chess. Philosoph. Mag. 41:256-75. Siewiorek, D., G. Bell, and A. Newell. 1981. Computer Structures: Principles and Examples. New York: McGraw-Hill. Simon, H. A. 1993. Allen Newell: The entry into complex information processing . Artif. Intell. 59:251-59. Simon, H. A. 1996. Models of My Life. Cambridge, Mass.: The MIT Press. Walter, W. G. 1953. The Living Brain. New York: Norton. Waterman, D. A., and A. Newell. 1971. Protocol analysis as a task for artificial intelligence. Artif Intell. 2:285-318.

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