Stuart E. Dreyfus

Bio: Stuart E. Dreyfus is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Dynamic programming & Dreyfus model of skill acquisition. The author has an hindex of 32, co-authored 82 publications receiving 13943 citations. Previous affiliations of Stuart E. Dreyfus include RAND Corporation.

Mind over machine: The power of human intuition and expertise in the era of the computer

Abstract: Computers are being used more and more in all aspects of our lives and, programmed correctly, they are more accurate and precise than humans can ever be. Here, however, the myth of the superiority of artificial intelligence is examined and dispelled. The authors, one a philosopher and the other a computer scientist, argue that even highly advanced systems only correspond to the very early stages of human learning and that there are many human skills that computers will never be able to emulate. The mind will always be superior to the machine. To illustrate their point, they set forth a model documenting five distinct levels - novice, advanced beginner, competent, proficient and expert - through which human beings pass in acquiring and mastering a skill. The two final stages require a degree of intuitive intelligence far beyond the most ambitious projects being planned for the future. The authors acknowledge the huge progress made by computers and the massive advantages to be gained from using them, but they stress that their value can only lie in their use as aids, never as substitutes for the human mind.

A Five-Stage Model of the Mental Activities Involved in Directed Skill Acquisition

Abstract: : In acquiring a skill by means of instruction and experience, the student normally passes through five developmental stages which we designate novice, competence, proficiency, expertise and mastery. We argue, based on analysis of careful descriptions of skill acquisition, that as the student becomes skilled, he depends less on abstract principles and more on concrete experience. We systematize and illustrate the progressive changes in a performer's ways of seeing his task environment. We conclude that any skill- training procedure must be based on some model of skill acquisition, so that it can address, at each stage of training, the appropriate issues involved in facilitating advancement.

An Appraisal of Some Shortest-Path Algorithms

Abstract: This paper treats five discrete shortest-path problems: 1 determining the shortest path between two specified nodes of a network; 2 determining the shortest paths between all pairs of nodes of a network; 3 determining the second, third, etc., shortest path; 4 determining the fastest path through a network with travel times depending on the departure time; and 5 finding the shortest path between specified endpoints that passes through specified intermediate nodes. Existing good algorithms are identified while some others are modified to yield efficient procedures. Also, certain misrepresentations and errors in the literature are demonstrated.

Reinforcement Learning: An Introduction

Abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

Situated Cognition and the Culture of Learning

Abstract: Many teaching practices implicitly assume that conceptual knowledge can be abstracted from the situations in which it is learned and used. This article argues that this assumption inevitably limits the effectiveness of such practices. Drawing on recent research into cognition as it is manifest in everyday activity, the authors argue that knowledge is situated, being in part a product of the activity, context, and culture in which it is developed and used. They discuss how this view of knowledge affects our understanding of learning, and they note that conventional schooling too often ignores the influence of school culture on what is learned in school. As an alternative to conventional practices, they propose cognitive apprenticeship (Collins, Brown, & Newman, in press), which honors the situated nature of knowledge. They examine two examples of mathematics instruction that exhibit certain key features of this approach to teaching.

A Formal Basis for the Heuristic Determination of Minimum Cost Paths

Abstract: Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.

Five Misunderstandings About Case-Study Research

Abstract: This article examines five common misunderstandings about case-study research: (a) theoretical knowledge is more valuable than practical knowledge; (b) one cannot generalize from a single case, therefore, the single-case study cannot contribute to scientific development; (c) the case study is most useful for generating hypotheses, whereas other methods are more suitable for hypotheses testing and theory building; (d) the case study contains a bias toward verification; and (e) it is often difficult to summarize specific case studies. This article explains and corrects these misunderstandings one by one and concludes with the Kuhnian insight that a scientific discipline without a large number of thoroughly executed case studies is a discipline without systematic production of exemplars, and a discipline without exemplars is an ineffective one. Social science may be strengthened by the execution of a greater number of good case studies.