Allen Newell

The Classic Study of Cognition by Two Pioneers of Artificial Intelligence First published in 1972, this monumental work develops and defends the authors' information processing theory of human reasoning. Human reasoners, they argue, can be modeled as symbolic "information processing systems" (IPSs), abstracted entirely from physiological bases. Modeling subjects with IPSs yields predictive theories of their problem-solving behavior and performance, and psychological insight into their heuristics and methods.Newell and Simon's previous epoch-making collaborations included the General Problem Solver, the Logic Theorist, and the Information Processing Language. This book is a careful application of those ideas from artificial intelligence - the ideas of AI's first golden age - to cognitive psychology. The authors first develop the formal theory of information processing systems. They then report studies of three symbolic reasoning tasks, and analyze that data using the information processing paradigm. In the final section, they state their comprehensive theory of human problem-solving. The success of the models of cognition given in Human Problem Solving was a major piece of evidence for the physical symbol system hypothesis, which Newell and Simon would first state a few years later. Newell went on to co-develop the Soar cognitive architecture, and Simon to receive the Nobel Prize in Economics. The two jointly received the Turing Award in 1975 for the research program of which Human Problem Solving was the culmination.This book is also available from Echo Point Books as a hardcover (ISBN 1635617928).

First published in 1972, this monumental work develops and defends the authors' information processing theory of human reasoning. Human reasoners, they argue, can be modeled as symbolic "information processing systems" (IPSs), abstracted entirely from physiological bases. Modeling subjects with IPSs yields predictive theories of their problem-solving behavior and performance, and psychological insight into their heuristics and methods. Newell and Simon's previous epoch-making collaborations included the General Problem Solver, the Logic Theorist, and the Information Processing Language. This book is a careful application of those ideas from artificial intelligence - the ideas of AI's first golden age - to cognitive psychology. The authors first develop the formal theory of information processing systems. They then report studies of three symbolic reasoning tasks, and analyze that data using the information processing paradigm. In the final section, they state their comprehensive theory of human problem-solving. The success of the models of cognition given in this work was a major piece of evidence for the physical symbol system hypothesis, which Newell and Simon would first state a few years later. Newell went on to co-develop the Soar cognitive architecture, and Simon to receive the Nobel Prize in Economics. The two jointly received the Turing Award in 1975 for the research program of which Human Problem Solving was the culmination.

Rarely do research paths diverge and converge as neatly and productively as the paths exemplified by the two efforts contained in this book. The story behind these researches is worth recounting. The story, as far as I'm concerned, starts back in the Fall of1976, when John Laird and Paul Rosenbloom, as new graduate students in computer science at Carnegie-Mellon University, joined the Instructible Production System (IPS) project (Rychener, Forgy, Langley, McDermott, Newell, Ramakrishna, 1977; Rychener & Newell, 1978). In those days, production systems were either small or special or both (Newell, 1973; Shortliffe, 1976). Mike Rychener had just completed his thesis (Rychener, 1976), showing how production systems could effectively and perspicuously program the full array of artificial intelligence (AI) systems, by creating versions of Studellt (done in an earlier study, Rychener 1975), EPAM, GPS, King-Pawn-King endgames, a toy-blocks problem solver, and a natural-language input system that connected to the blocks-world system.

Psychology is now ready for unified theories of cognition—so says Allen Newell, a leading investigator in computer science and cognitive psychology. Not everyone will agree on a single set of mechanisms that will explain the full range of human cognition, but such theories are within reach and we should strive to articulate them.In this book, Newell makes the case for unified theories by setting forth a candidate. After reviewing the foundational concepts of cognitive science—knowledge, representation, computation, symbols, architecture, intelligence, and search—Newell introduces Soar, an architecture for general cognition. A pioneer system in artificial intelligence, Soar is the first problem solver to create its own subgoals and learn continuously from its own experience.Newell shows how Soar’s ability to operate within the real-time constraints of intelligent behavior, such as immediate-response and item-recognition tasks, illustrates important characteristics of the human cognitive structure. Throughout, Soar remains an exemplar: we know only enough to work toward a fully developed theory of cognition, but Soar’s success so far establishes the viability of the enterprise.Given its integrative approach, Unified Theories of Cognition will be of tremendous interest to researchers in a variety of fields, including cognitive science, artificial intelligence, psychology, and computer science. This exploration of the nature of mind, one of the great problems of philosophy, should also transcend disciplines and attract a large scientific audience.

Rarely do research paths diverge and converge as neatly and productively as the paths exemplified by the two efforts contained in this book. The story behind these researches is worth recounting. The story, as far as I'm concerned, starts back in the Fall of1976, when John Laird and Paul Rosenbloom, as new graduate students in computer science at Carnegie-Mellon University, joined the Instructible Production System (IPS) project (Rychener, Forgy, Langley, McDermott, Newell, Ramakrishna, 1977; Rychener & Newell, 1978). In those days, production systems were either small or special or both (Newell, 1973; Shortliffe, 1976). Mike Rychener had just completed his thesis (Rychener, 1976), showing how production systems could effectively and perspicuously program the full array of artificial intelligence (AI) systems, by creating versions of Studellt (done in an earlier study, Rychener 1975), EPAM, GPS, King-Pawn-King endgames, a toy-blocks problem solver, and a natural-language input system that connected to the blocks-world system.