NEWELL, ALLEN

Newell was a founder of artificial intelligence (AI) and a pioneer in the use of computer simulations in psychology. In collaboration with J. Cliff Shaw and Herbert

A. Simon, Newell developed the first list-processing programming language as well as the earliest computer programs for simulating human problem solving. Over a long and prolific career, he contributed to many techniques, such as protocol analysis and heuristic search, that later became part of psychology and computer science. Colleagues remember Newell for his deep commitment to science, his care for details, and his inexhaustible energy.

Education . Newell was the second and youngest son of Jeanette Le Valley Newell and Robert R. Newell, a professor of radiology at Stanford Medical School. At age sixteen, Allen Newell fell in love with Noël McKenna, a fellow high school student. They were married four years later and had a son, Paul.

Although he admired his father, whom he described as a “complete man,” Newell did not grow up planning to be a scientist. After completing one quarter at Stanford University, at the end of World War II, he was drafted into the navy. His father, Robert Newell, belonged to a team of scientists involved in the study of the Bikini Atoll nuclear tests, and Robert asked that his son Allen be assigned to assist the team. Allen’s contribution was to write maps of the radiation distribution over the atolls. This exciting endeavor ignited Newell’s interest in science. Back from the navy, he reenrolled in Stanford University, where he majored in physics. Still fascinated with radiation, he spent much of his college time working on x-ray microscopy as a research assistant.

As a freshman, Newell took a course in which George Polya, a distinguished mathematician, covered his book How to Solve It (1945); Newell was so fascinated that he took many more courses with him. Polya’s book concerns heuristics—plausible methods for solving problems. Unlike algorithmic methods, which are always guaranteed to solve every solvable instance of a problem, heuristic methods may or may not lead to the best solution. But for many problems, algorithms are either unavailable or impractical. In such cases, heuristic methods are needed. Heuristic methods were to play a critical role in Newell’s later research.

In 1949, Newell went from Stanford to Princeton, to pursue graduate studies in mathematics. As a research associate for Oskar Morgenstern, he worked on stochastic models of logistic problems and on game theory, which had recently been invented by Morgenstern with John von Neumann. But pure mathematics was not for Newell. By the end of his first year, he left Princeton for a job at RAND Corporation in Santa Monica, California. RAND was a recently founded think tank, devoted to research with military applications and funded mainly by the U.S. Air Force. As a member of RAND’s Mathematics Division, Newell was free to pursue research that interested him. He could also collaborate with the many talented scientists who worked at RAND, as well as dozens of external consultants who visited every summer.

RAND Corporation RAND provided Newell with several formative experiences. With Joseph B. Kruskal, he coauthored two reports in which they applied formal game-theoretic methods to organization theory. He spent six weeks in Washington, DC, visiting the Munitions Board, which was responsible for logistics at the Pentagon. Upon his return, Newell wrote a report proposing a research program for the “science of supply.” He also designed and conducted experiments on decision making in small groups, experiments along the lines of those by Fred Bales, a Harvard social psychologist and RAND consultant. Eventually, Newell joined an ambitious team of psychologists devoted to the experimental study of human organizational behavior.

Besides Newell, the group consisted of William C. Biel, Robert Chapman, and John L. Kennedy. They built and studied a full-scale simulation of an Air Defense Early Warning Station, whose crew had to observe radar signals and decide whether to send planes to investigate them. The simulation was aimed at understanding the crew’s interactions with radar screens, interception aircraft, and each other. The technique for doing so, which involved tape-recording and analyzing the crew’s phone conversations, prefigured Newell’s later work on human “thinking-aloud” protocols. This research led to a training program for the Air Defense Command and to the creation of the System Development Corporation to implement it. For security reasons, the group published little. Nevertheless, for this work, Biel, Chapman, Kennedy, and Newell shared the 1979 Alexander C. Williams Jr. Award from the Human Factors Society.

Within the group, Newell was in charge of simulating air traffic radar displays, which had to be realistic and continuously evolving in time. To do so, he enlisted the help of Cliff Shaw, a RAND programmer who became a long-term collaborator. Newell and Shaw programmed an IBM Card-Programmed Calculator to generate the successive radar displays, based on data from actual flight patterns, which gave them early experience using computing machines for non-numerical tasks—an activity that became paramount to Newell’s research.

The 1950s decade was the heyday of cybernetics, information theory, and automata theory. The first modern computers were being built, and a hot new idea was the analogy between mental and computational processes. The analogy was initially proposed by Warren S. McCulloch and Walter Pitts. It was quickly adopted and developed by Norbert Wiener, John von Neumann, and other scientists, whose work would have an impact on Newell.

In 1936, Alan M. Turing had proposed a formal model of computation that became the foundation of theoretical computer science. In 1943, McCulloch and Pitts argued that neurons were simple logic devices such that mental processes could be explained by computations performed by the brain. In the late 1940s, several laboratories, including one at RAND, began building modern digital computers—“electronic brains,” as they became popularly known. In 1948, Norbert Wiener’s Cybernetics offered a vision for a new science of minds and machines, and the publication of Claude Shannon’s mathematical theory of information generated considerable excitement. Turing argued in 1950 that computers could be programmed to behave as intelligently as humans (although Newell did not learn of this until much later). Ross Ashby offered an influential synthesis of these ideas in his book Design for a Brain(1952). In 1949, William Grey-Walter built mechanical turtles that plugged themselves into electrical outlets when their batteries ran low; a version of them could be seen crawling around RAND offices. By the early 1950s several people took steps towards artificial intelligence (AI): Turing and Shannon sketched designs for chess-playing computers; Arthur Samuel programmed a computer to play checkers; and Oliver Selfridge and G. P. Dinneen wrote computer programs to do pattern recognition and learning. During his five years of residence at RAND, Newell absorbed these new ideas, which were ushering in the information-processing revolution and the birth of AI.

Another man attracted by this new culture of information processing and automata was Herbert Simon, who was then an established social scientist at Carnegie Institute of Technology (later Carnegie Mellon University), in Pittsburgh, Pennsylvania. Simon was a consultant for Newell’s group at RAND, and the two became lifelong friends. In 1954, Newell decided to move to Pittsburgh and write a PhD dissertation under Simon. Before leaving RAND, Newell attended a seminar by Selfridge on his work with Dinneen on computer learning and pattern recognition. Impressed with Selfridge’s demonstration that a computer could exhibit such intelligent behavior, Newell had what he described as a “conversion experience”: He envisioned programming computers to act as intelligent agents. He decided to devote all his energies to the task of understanding the human mind by simulating it. This remained the primary focus of his research for the rest of his life.

Computers as Intelligent Agents Newell immediately began working on his new research program. During the following four months, he wrote a computer program designed to play chess in a way that resembled human playing. The program was a step toward Newell’s characteristic methodology of cognitive simulation: drawing ideas from psychology in programming and understanding the mind by building one. With Simon, he later argued that a program that simulates a cognitive process constitutes a rigorous theory of the process, and that psychological theories should be accompanied by computer simulations. Newell did not manage to implement his first chess program, but some of his ideas came to fruition in a later chess program (1958).

In 1955, while remaining affiliated with RAND, Newell moved to Carnegie Tech. He defended his dissertation in 1957 and became institute professor in 1961. He never moved again—he stayed at Carnegie Tech even during sabbaticals. The last five years of the 1950s saw Newell—together with Shaw (still at RAND) and

Simon—producing the work on AI that propelled the Carnegie-RAND group to international fame: Logic Theorist, General Problem Solver, list processing, and protocol analysis.

Logic Theorist (LT) was designed to discover proofs of the logical theorems contained in Chapter 2 of Alfred

N. Whitehead and Bertrand Russell’s Principia Mathematica (1910–1913). LT represented axioms and theorems by symbolic structures (called states) and modified them by applying suitable operators. But LT did not attempt every combination of symbols and operators— there was not enough speed and memory for that. Also, its method was not designed to find proofs in the most efficient way possible. Rather, LT’s heuristic method attempted to mimic human discovery: It started with the theorem to be proved, and then it searched for axioms and operators from which to derive the theorem. It managed to prove thirty-eight out of fifty-two theorems.

LT was simulated by hand in December 1955 and produced the first mechanical proof of a theorem on 9 August 1956. It was the only running program presented at the Dartmouth Conference of 1956—the first conference explicitly devoted to AI—and it was the first mechanical theorem-prover. It was part of an ambitious research program of simulating human thinking, and it came with a novel way to program computers.

At the time, there were no high-level programming languages. Computers were programmed in a language very close to the 1s and 0s manipulated by the processor. This made it difficult to define non-numerical symbolic structures, such as the logical formulae manipulated by LT. Furthermore, computer memories were very small, which made it difficult to use computers for tasks, such as heuristic search, that required a varying and unpredictable amount of memory. To overcome these and other obstacles, Newell and his collaborators invented the first list-processing language. A list is like a simple associative memory, in which one symbol has a link to another, which links to another, and so on. In list processing, the lists—the symbols and links—are created and modified to generate structures of (in principle) unlimited length and complexity. Many of the ideas introduced with list processing became fundamental to computer science. One of the Dartmouth organizers, John McCarthy, went on to write Lisp, an improved list-processing language that became standard in AI.

To develop and test models of human thinking, data were needed. Neuroscience was not advanced enough to provide them, and introspection was considered unreliable. Mainstream experimental psychologists recorded only behaviors and reaction times during simple tasks, without asking subjects for clues as to what they were thinking. To Newell and Simon, these traditional data sources seemed insufficient to understand human problem solving. Hence, they revived introspection in the form of protocol analysis. They asked subjects to think aloud while doing their logical derivation or other task, and they recorded their subjects’ speech. They devised procedures for both extracting information about thought processes from the protocols and testing the validity of the protocols. Protocol analysis provided the main ground for testing the accuracy of Newell and Simon’s simulations, and it expanded the range of evidence available to psychologists.

In 1957, by analyzing the protocol of a subject doing logic derivations, Newell and Simon discovered a general heuristic method for solving problems. They dubbed it means-ends analysis: A subject compares the current state of the problem with a goal state (the ends), finds the difference between them, searches in memory for operators that might reduce this kind of difference (the means), and applies them to the current state. The process is repeated until either all differences are eliminated—and the problem is solved—or the subject gives up. In the latter case, the available resources (operators, time, etc.) have been used, but the problem remains unsolved.

Means-ends analysis became the core of a theory of human problem solving and of General Problem Solver (GPS), a program that was more powerful and general than LT and other AI programs at the time. Unlike LT, which specialized in logical theorems, GPS could solve problems in different domains. All it needed was a way to represent the domain, operators for manipulating the representations, and information about which operators could reduce which differences. To some extent, GPS could even construct new operators from a set of primitives and learn which operators reduced which differences. After GPS, means-ends analysis became widely used in AI.

Sometimes, GPS would get lost in the search for a solution to a subproblem, digging itself into a processing hole without exit. The Carnegie-RAND group thought this pitfall might be addressed by writing into each operator the conditions for its correct application. In this way, operators could be applied to relevant situations without requiring searches that risked being endless. Each instruction would take the form of an “if-then” statement—“if things are so and so, then do such and such”—and would be applied automatically if and only if its conditions were satisfied. The result of each operation would be deposited in a central memory storage, or working memory, on which all operators could write. Instructions of this if-then form are called productions and constitute production systems, a general programming style invented by logician Emil Post (1943) and adapted to computer science by Robert Floyd (1961). Newell took production systems to heart as a potent new way of programming. He developed a succession of production system languages, which he and others used to build AI systems.

Unified Theories of Cognition From the 1960s, Newell participated in many collaborative research projects in computer science. One of the most significant projects led to a technique for comparing computer architectures. Architecture is the set of fixed mechanisms and organizing principles of a computer. Together with the software that runs on it, a computer’s architecture explains its behavior. In 1968, Newell agreed to help Gordon Bell write a textbook on computer architectures (first edition, 1972; revised edition, with Dan Siewiorek, 1981).

In order to classify and compare different computer architectures, Newell and Bell distinguished different levels of analysis—descriptions of computers and their behavior containing different amounts of detail—and devised general languages for two important levels. One was the system level, which is constituted by the main components of a computer, such as processors, memories, and links between them. The other was the instruction level, which is constituted by the primitive operations performed by a processor and the set of primitive instructions that drive them. Newell and Bell’s language for the instruction level, ISP (Instruction Set Processor) later made it possible for different computers to simulate each other. As long as one computer had an ISP description and another could run an interpreter for ISP, the second computer could simulate the first by running its ISP description.

Another important project developed by Newell led to a theory of human-computer interaction. In 1970, the Xerox Palo Alto Research Center—a center on digital technology—was formed in Palo Alto, California. Newell, a consultant for the center, proposed to study the way that users interact with computers. So, in 1974, Stuart Card and Thomas Moran—two of Newell’s students—moved to Palo Alto. Together with Newell, they collected a wide range of psychological data and theories—such as Fitts’s Law, the power law of learning, and models of human typing—and unified them into a general model of routine cognitive skills that could be used by designers of computer interfaces (published in 1983).

The model described the human cognitive architecture, which was assumed to include perceptual, motor, and cognitive processors as well as memories to store data. The model estimated the main functional characteristics of the components, such as the time required for a processor’s cycle and the size of the memories. The model was also associated with a methodology for analyzing a computer user’s routine task by separating the task into the basic processes necessary to perform it. Using this methodology, a designer could approximately predict the time it would take a user to perform a certain routine task, such as typing a piece of text or using the mouse to reach a target. Modeling the routine cognitive skills of human beings required a synthesis of many psychological data and theories. This provided a blueprint and some impetus for Newell’s final and most ambitious project: a theory of the widest possible range of psychological phenomena, a unified theory of cognition.

Newell saw the science of psychology as fragmented: Practitioners specialized in a narrow range of phenomena, such as some aspect of perception, memory, or reasoning; perhaps they even developed a specialized theory to explain the phenomena. But starting in the 1970s, Newell argued that psychologists could aim higher. Newell saw minds and computers as knowledge systems, namely, systems that may be understood in terms of the content of their beliefs and goals. At the knowledge level—the most abstract level of analysis for intelligent systems—behavior is predicted and explained by assuming that the system pursues goals in light of its beliefs. According to Newell, knowledge systems are implemented—always imperfectly—by physical symbol systems, that is, systems that generate behavior by executing programs on symbolic structures. From his extensive experience with artificial computers, he knew that computers could be understood in terms of their architecture (the fixed mechanisms) plus their programs (the software). Newell argued that the human mind could be understood in the same way, provided that psychologists go beyond their narrow specializations and bring existing psychological data and theories to bear on the nature of the human cognitive architecture.

As a vehicle for his unified theory of cognition, Newell chose Soar, a production systems architecture for general intelligence that he and his students John Laird and Paul Rosenbloom began working on around the early 1980s. The core of Soar as a unified theory is a general-purpose problem solver. To a first approximation, it is a production systems version of GPS. The system learns by chunking, a notion that takes George Miller’s classic notion of an information chunk (1956) and extends it to procedural learning: After it solves a problem, Soar creates a new instruction in the form of a production, which summarizes what needs to be done to solve that problem. The next time it encounters that problem, Soar may use this new production, without having to solve the problem anew. Thus, Soar’s chunking explains some types of learning.

In developing his unified theory, Newell took into account as many architectural constraints as possible: from neuroscience (size and speed of components), psychology (behaviors and reaction times), and computer science (features of symbol processing). He and his collaborators developed their theory into explanations of many psychological phenomena at many temporal scales, from simple behaviors, such as pressing a button when a light goes on, to solving difficult problems, such as cryptarithmetic puzzles. Although Newell’s unified theory aims at bridging levels and satisfying multiple constraints, Newell did not attempt to explain how the architecture he proposed may be implemented in the human brain.

Newell subsumed much of his previous work—on problem solving, human-computer interaction, and computer architectures—within Soar as a unified theory. In addition, many important psychological phenomena and some influential mid-range theories (such as Philip Johnson-Laird’s theory of mental models) found a place within Soar. Newell worked on Soar until his death from cancer in 1992 at the age of sixty-five. After that, work continued in several laboratories around the world. Soar made possible the creation of virtual human beings, such as synthetic pilots that behaved as much as possible like human pilots.

Newell led the development of computer science, AI, and cognitive science as both disciplines and institutions, and his peers recognized his role in research and service. His long list of honors includes: the Harry Goode Memorial Award of the American Federation of Information Processing Societies (1971); the A. M. Turing Award of the Association of Computing Machinery (1975, with Herbert Simon); founding president of the American Association for Artificial Intelligence (1980); the Distinguished Scientific Contribution Award of the American Psychological Association (1985); and in 1992, one month before his death, the National Medal of Science.

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Newell, Allen

Computer Sciences
COPYRIGHT 2002 The Gale Group Inc.

Newell, Allen

Newell, Allen

American Scientist and Mathematician1927–1992

A scientist and mathematician, Allen Newell is best remembered for his work and research on artificial intelligence (AI) . Some of his most well known initiatives include the Logic Theorem Machine, a mechanical device that would be used to create new theorems, as well as the SOAR project, a research initiative that attempted to implement cognitive or rule-based computer simulations.

Newell was born in San Francisco, California, 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. He attended Lowell High School—the intellectual high school of San Francisco—where he was inspired academically and fell in love (at age sixteen) with fellow student
Noel McKenna. Newell and McKenna married at age twenty and remained married for forty-five years.

Newell had no intention of following a scientific career upon graduation from high school. However, after working a summer in a shipyard, he enlisted in the U.S. Navy, and it was during his tenure in the navy that he became interested in scientific enterprise. He was serving on a ship that carried scientific observers to the Bikini atoll (island) to study the effect of nuclear tests, and Newell was assigned the task of mapping the radiation distribution over the atolls. Newell discovered how exciting science could be, and thereafter, he characterized himself simply as a scientist.

Newell received his bachelor of science degree in physics from Stanford University in 1949, spent a year at Princeton doing graduate work in mathematics, and obtained a Ph.D. from Carnegie Institute of Technology (now Carnegie Mellon University) in industrial administration in 1957.

Newell's primary interest, like that of his colleague Herbert A. Simon (1916–2001), was in understanding human intelligence and cognition. He developed the SOAR project with students and colleagues, including John E. Laird, a professor at the University of Michigan, and Paul S. Rosen-bloom, a professor at the University of Southern California, and others. Essentially, SOAR was a rule-based computer simulation or emulation of a cognitive system that was capable of learning and solving problems. The rules are defined by such structures as "If … then …," similar to structures that are thought by some to govern human behavior.

Like Simon, Newell was primarily interested in organizations and their behavior, but he soon moved toward individual cognition. He and Simon had met while Newell was working for the RAND Corporation in Santa Monica, California. It was Simon who influenced Newell to come to Carnegie Tech to obtain a doctorate. Simon and Newell collaborated, and Newell continued to work for RAND in Pittsburgh, as a one-man "office" until he became part of the Carnegie Institute faculty in 1961.

Newell's interest in human learning and thinking was also spurred by Oliver Selfridge, an artificial intelligence researcher, who created theories on pattern recognition , that is, the recognition of letters and other patterns. This led Newell to think of computing as a symbolic manipulation, rather than an arithmetic one, and led him to write a chess playing program c. 1955, which was then implemented by himself, Simon, and John Clifford Shaw in 1956.

Newell also collaborated with Simon and Shaw on the Logic Theorem Machine, a program to find or develop theorems. The theorems were discovered by working backward from the theorem to the axioms in an inductive method of discovery, looking for patterns or regularity in the data. This was an interest that would last his entire life. Newell was also a member of the initial Dartmouth conference, considered to be the first conference in artificial intelligence, along with Simon, Marvin Minsky, John McCarthy, and others.

Newell won the A. M. Turing Award with Simon in 1975. He was also the recipient of the first Award for Research Excellence from the International Joint Conference on Artificial Intelligence and was elected the first president of the American Association for Artificial Intelligence.

With Stuart Card and Thomas Moran, Newell also participated in some of the early research in Human Computer Interaction. This involved the GOMS system of Goals, Operators, Methods, and Selection, a structure for studying human behavior with the computer (and other machines, e.g., calculators) as well as the performance of any task. He also developed the mechanism, with Simon, of "talking out loud" to study the way people solve problems, and the pair was instrumental in developing means-end analysis, a way of explaining how people solve problems that is based on the theory that people notice a discrepancy between their current state and some goal state and employ some operator or operation to remove or overcome the difference.

With Shaw and Simon, Newell developed the information processing languages (IPL-I through IPL-V), which, although not as popular as LISP, were early languages for artificial intelligence. He later took a lead in the effort to develop OPS5 (and other OPS languages), a rule-based language for building artificial systems such as expert systems. His final project, ongoing after his death, was, however, the SOAR system. This was a system that purported to give an architecture of cognition, meant to explore the nature of a unified theory of cognition, a "mental" architecture.

see also Artificial Intelligence; Decision Support Systems; Simon, Herbert A.

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Newell, Allen

Newell was the second child of Robert R. Newell, a radiologist, and Jeanette Levalley, a homemaker. Graduating from Lowell High School in 1945 after a career marked more by his ability in football than by intellectual pursuits, Newell enlisted in the U.S. Navy. Assigned to the chore of making maps of the radiation emitted during the Bikini Atoll nuclear tests, Newell realized that he loved doing scientific work. He accordingly, after his discharge in 1946, enrolled at Stanford University, where he copublished an article on X-ray optics. In December 1947 Newell married his high school sweetheart, Noel McKenna. They had one son. Newell earned a bachelor’s degree in 1949 and began studying mathematics at Princeton University in New Jersey.

At Princeton, Newell’s interests soon turned away from theoretical math and toward experimentation. Consequently, he took a leave of absence from Princeton and went to work for the newly formed Rand Corporation in Santa Monica, California. One of the papers he wrote during this period, “Observations on the Science of Supply” (Technical Report D-926, Rand Corporation, 1951), focuses directly on the weakness of theoretical models to comprehend the realities of production and distribution systems. While working over the next few years on a Rand study of the U.S. Air Force early warning system, Newell and a colleague, Cliff Shaw, solved a basic problem: how to use a computer to simulate radar images on an early warning screen. Their achievement represented a major step not only toward Newell’s ultimate work in artificial intelligence (AI) but also toward the development of modern video games.

In September 1954 Newell experienced what he later described as a “conversion experience.” At a Rand seminar he learned that computer systems could not only create images but recognize them as well. His reaction was embodied in a classic paper, “The Chess Machine: An Example of Dealing with a Complex Task by Adaptation” (Proceedings of the Western Joint Computer Conference, 1955), which embodied nearly all the key features of what became known as an expert system: aspiration levels, “good enough” moves, generation of subgoals, and a coherent symbol system with which to represent the playing surface. Newell moved to the Carnegie Institute of Technology (now Carnegie-Mellon University) in Pittsburgh, where he developed for Rand the logic theory machine in 1956. He worked with the Nobel Prize winner Herbert Simon toward a Ph.D., which Newell received in 1957. Perhaps the culmination of this fertile period was his invention of Information Processing Languages (IPL), the first list-processing languages. These languages introduced lists, associations, frames, streams, and associative retrieval, without which the achievements in AI of the next decade might have been impossible. His 1961 manual for the IPL-V was the first to advocate “top-down” programming. Another prime goal of AI research had been reached: a language that “relies heavily on heuristic methods similar to those that have been found in human problem solving activity.”

Newell continued his dual role as Carnegie researcher and Rand employee until 1961, when he became a full-time professor in charge of developing Carnegie-Mellon’s School of Computer Science. His own doctoral degree had been awarded in industrial administration because no appropriate title for his work existed at that time. Working closely with Shaw and Simon, he produced the effective chess-playing program he had theorized earlier. Although it was no IBM Deep Blue—a ten-year-old once beat it— it provided the experimental validation that Newell always demanded from theoretical Schemas. In 1972 he and Simon finally put all their accumulated insights into a book, Human Problem Solving, which remained a standard in its field into the twenty-first century.

Newell believed that the physical architecture of the computer was not the key to machine intelligence, although he acknowledged that the development of massively parallel computing systems made the kind of processing he was designing far simpler. He and Gordon Bell coauthored a book on such systems, Computer Structures: Readings and Examples (1971). Again pushing theoretical insights toward their practical applications, Newell collaborated on L*, a new language based on the insights of his book, and a new type of menu system called ZOG, which ultimately was used on the aircraft carrierCarl Vinson in 1982.

During this period Newell also tackled a Xerox PARC project analyzing cognitive skills, the heart of which was embodied in the acronym GOMS: goals, operators, methods, and selection. His exposition of this project in The Psychology of Human-Computer Interaction (1983), which he wrote with Stuart K. Card and Thomas P. Moran, was a key step in the attainment of Newell’s ultimate goal, a broad-based theory of cognition. His first real breakthrough in this direction was Soar, a cognitive architecture capable of solving problems and learning in a human way. Other models of cognition focus on verbal memory, sense perceptions, or the creation of concepts, but Soar focuses on problem solving. As Simon said, “The Soar program is a production system.”

After Newell’s death the Soar project continued at Carnegie-Mellon University, the University of Southern California, the University of Michigan, and other centers of research on artificial intelligence. Soar works effectively because of two innovations, “chunking” and the use of a “weak” method of problem solving. With its memory defined as a set of relatively discrete chunks rather than as an unbreakable whole, the system can adapt to new information or concepts without rebuilding the entire memory. By relying on a problem-solving formula that is relatively weak, the system becomes less “stubborn,” turning away from time-wasting problems and trying to resolve them a different way. Invited to deliver the William James Lectures at Harvard University in 1987, Newell sketched out the advantages of the Soar system. He also used Soar as the model in his final book, Unified Theories of Cognition (1990).

When the American Association for Artificial Intelligence was founded in 1979, Newell was elected its first president, and he also headed the Cognitive Science Society. A month before his death he received the National Medal of Science. In 1975 he and Simon received the A. M. Turing Award from the Association for Computing Machinery, which established an annual award in Newell’s honor after his death. Newell’s other honors included awards from the American Federation of Information Processing Societies (1971), the American Psychological Association (1985), the Institute of Electrical and Electronics Engineers (1990), the Franklin Institute (1992), and honorary degrees from the University of Pennsylvania and Groningen University. Newell died of cancer in 1992.

With his muscular build and six-foot, one-inch frame, Newell stood out among computer scientists, who are often more dedicated to intellectual, rather than physical, development. He was renowned for his work ethic, often working through the night to solve a problem. As he said in 1991, “There is no substitute for working hard—very hard.” One of his few leisure pursuits was watching football games on television on the weekends, a reminder of his high school avocation. A man of prodigious energy and endurance, focused on the impalpable workings of the brain, Allen Newell was a rare combination.

For more information on Newell see Pamela McCorduck, Machines Who Think (1979), and J. Laird and P. Rosenbloom, “In Pursuit of Mind: The Research of Allen Newell,” AI Magazine 13, no. 4 (1992): 17-45. An obituary is in the New York Times (20 July 1992).

Hartley S. Spatt

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