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Author: K.J. Gilhooly
Publisher: Springer Science & Business Media
ISBN: 1468480154
Category : Psychology
Languages : en
Pages : 391
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Book Description
Problem solving is a central topic for both cognitive psychology and artificial intelligence (AI). Psychology seeks to analyze naturally occur ring problem solving into hypothetical processes, while AI seeks to synthesize problem-solving performance from well-defined processes. Psychology may suggest possible processes to AI and, in turn, AI may suggest plausible hypotheses to psychology. It should be useful for both sides to have some idea of the other's contribution-hence this book, which brings together overviews of psychological and AI re search in major areas of problem solving. At a more general level, this book is intended to be a contribution toward comparative cognitive science. Cognitive science is the study of intelligent systems, whether natural or artificial, and treats both organ isms and computers as types of information-processing systems. Clearly, humans and typical current computers have rather different functional or cognitive architectures. Thus, insights into the role of cognitive ar chitecture in performance may be gained by comparing typical human problem solving with efficient machine problem solving over a range of tasks. Readers may notice that there is little mention of connectionist ap proaches in this volume. This is because, at the time of writing, such approaches have had little or no impact on research at the problem solving level. Should a similar volume be produced in ten years or so, of course, a very different story may need to be told.
Author: K.J. Gilhooly
Publisher: Springer Science & Business Media
ISBN: 1468480154
Category : Psychology
Languages : en
Pages : 391
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Book Description
Problem solving is a central topic for both cognitive psychology and artificial intelligence (AI). Psychology seeks to analyze naturally occur ring problem solving into hypothetical processes, while AI seeks to synthesize problem-solving performance from well-defined processes. Psychology may suggest possible processes to AI and, in turn, AI may suggest plausible hypotheses to psychology. It should be useful for both sides to have some idea of the other's contribution-hence this book, which brings together overviews of psychological and AI re search in major areas of problem solving. At a more general level, this book is intended to be a contribution toward comparative cognitive science. Cognitive science is the study of intelligent systems, whether natural or artificial, and treats both organ isms and computers as types of information-processing systems. Clearly, humans and typical current computers have rather different functional or cognitive architectures. Thus, insights into the role of cognitive ar chitecture in performance may be gained by comparing typical human problem solving with efficient machine problem solving over a range of tasks. Readers may notice that there is little mention of connectionist ap proaches in this volume. This is because, at the time of writing, such approaches have had little or no impact on research at the problem solving level. Should a similar volume be produced in ten years or so, of course, a very different story may need to be told.
Author: K. J. Gilhooly
Publisher:
ISBN: 9781468480160
Category :
Languages : en
Pages : 404
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Book Description
Author: Brian Christian
Publisher: W. W. Norton & Company
ISBN: 039363583X
Category : Science
Languages : en
Pages : 459
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Book Description
A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.
Author: William Lawless
Publisher: Academic Press
ISBN: 0128223790
Category : Computers
Languages : en
Pages : 448
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Book Description
Human-Machine Shared Contexts considers the foundations, metrics, and applications of human-machine systems. Editors and authors debate whether machines, humans, and systems should speak only to each other, only to humans, or to both and how. The book establishes the meaning and operation of “shared contexts between humans and machines; it also explores how human-machine systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems composed of humans and machines. This book explores how user interventions may improve the context for autonomous machines operating in unfamiliar environments or when experiencing unanticipated events; how autonomous machines can be taught to explain contexts by reasoning, inferences, or causality, and decisions to humans relying on intuition; and for mutual context, how these machines may interdependently affect human awareness, teams and society, and how these "machines" may be affected in turn. In short, can context be mutually constructed and shared between machines and humans? The editors are interested in whether shared context follows when machines begin to think, or, like humans, develop subjective states that allow them to monitor and report on their interpretations of reality, forcing scientists to rethink the general model of human social behavior. If dependence on machine learning continues or grows, the public will also be interested in what happens to context shared by users, teams of humans and machines, or society when these machines malfunction. As scientists and engineers "think through this change in human terms," the ultimate goal is for AI to advance the performance of autonomous machines and teams of humans and machines for the betterment of society wherever these machines interact with humans or other machines. This book will be essential reading for professional, industrial, and military computer scientists and engineers; machine learning (ML) and artificial intelligence (AI) scientists and engineers, especially those engaged in research on autonomy, computational context, and human-machine shared contexts; advanced robotics scientists and engineers; scientists working with or interested in data issues for autonomous systems such as with the use of scarce data for training and operations with and without user interventions; social psychologists, scientists and physical research scientists pursuing models of shared context; modelers of the internet of things (IOT); systems of systems scientists and engineers and economists; scientists and engineers working with agent-based models (ABMs); policy specialists concerned with the impact of AI and ML on society and civilization; network scientists and engineers; applied mathematicians (e.g., holon theory, information theory); computational linguists; and blockchain scientists and engineers. Discusses the foundations, metrics, and applications of human-machine systems Considers advances and challenges in the performance of autonomous machines and teams of humans Debates theoretical human-machine ecosystem models and what happens when machines malfunction
Author: Kurt VanLehn
Publisher:
ISBN:
Category : Cognitive psychology
Languages : en
Pages : 18
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Book Description
People can discover new problem solving strategies on their own, without help from a teacher, text or other source. Many machine learning programs exist that discover strategies under similar conditions. Do we now have a sufficient set of computational models for understanding human strategy discoveries? This paper presents a detailed analysis of a human problem solving protocol that uncovers 10 cases of strategies being discovered. It is argued that most cases are adequately modeled by existing machine learning techniques, and several are not, which suggests some interesting research problems for machine learning. The paper has five parts. After a brief discussion of the methods of the analysis and the protocol, the protocol analysis is presented in enough detail to allow evaluation of the accuracy of the empirical claims. A subsequent section classifies the cases of strategy discovery found in the data are classified according to standard machine learning concepts. The last section indicates which types of learning exhibited by the subject have not yet been exhibited by machine learning systems. This leads to the view that strategy acquisition by a component human is like scientific theory formation, with the attendant tasks hypothesis generation. Although current machine learning models of strategy acquisition seem pale by comparison, there seems to be nothing stopping us from building machine learning systems with human-level capabilities for strategy discovery. Keywords: Strategy discovery; Skill acquisition; Machine learning; Cognitive science; Impasses-driven learning; Artificial intelligence; Problem-solving; Human factor engineering. (JG).
Author: Ana-Maria Oltețeanu
Publisher: Springer Nature
ISBN: 3030303225
Category : Computers
Languages : en
Pages : 282
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Book Description
How would you assemble a machine that can be creative, what would its cogs be? Starting from how humans do creative problem solving, the author has developed a framework to explore whether a diverse set of creative problem-solving tasks can be solved computationally using a unified set of principles. In this book she describes the implementation of related prototype AI systems, and the computational and empirical experiments conducted. The book will be of interest to researchers, graduate students, and laypeople engaged with ideas in artificial intelligence, cognitive science, and creativity.
Author: Morton Wagman
Publisher: Praeger
ISBN:
Category : Business & Economics
Languages : en
Pages : 264
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Book Description
Wagman gives a broad, structured, and detailed account of advancing intellectual developments in both psychological and computational theories of the nature of problem- solving. Known for originating the PLATO computer-based Dilemma Counseling System, psychologist Wagman is the author of 17 books, including Scientific Discovery Processes in Humans and Computers (Praeger, 2000). In this book, Professor Emeritus Morton Wagman gives a broad, structured, and detailed account of advancing intellectual developments in both psychological and computational theories of the nature of problem solving. Known for originating the PLATO computer-based Dilemma Counseling System, psychologist Wagman is the author of 17 books, including Scientific Discovery Processes in Humans and Computers, (Praeger, 2000) Of special interest to readers will be Wagman's conclusion that artificial intelligence problem-solving systems are deepening and broadening theories of human problem solving from scientific to everyday approaches. Scholars and professionals in psychology, artificial intelligence, and cognitive science will consider this a volume a valuable addition to their collections.
Author: ALAN H. SCHOENFELD
Publisher: Elsevier
ISBN: 1483295486
Category : Mathematics
Languages : en
Pages : 426
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Book Description
This book is addressed to people with research interests in the nature of mathematical thinking at any level, topeople with an interest in "higher-order thinking skills" in any domain, and to all mathematics teachers. The focal point of the book is a framework for the analysis of complex problem-solving behavior. That framework is presented in Part One, which consists of Chapters 1 through 5. It describes four qualitatively different aspects of complex intellectual activity: cognitive resources, the body of facts and procedures at one's disposal; heuristics, "rules of thumb" for making progress in difficult situations; control, having to do with the efficiency with which individuals utilize the knowledge at their disposal; and belief systems, one's perspectives regarding the nature of a discipline and how one goes about working in it. Part Two of the book, consisting of Chapters 6 through 10, presents a series of empirical studies that flesh out the analytical framework. These studies document the ways that competent problem solvers make the most of the knowledge at their disposal. They include observations of students, indicating some typical roadblocks to success. Data taken from students before and after a series of intensive problem-solving courses document the kinds of learning that can result from carefully designed instruction. Finally, observations made in typical high school classrooms serve to indicate some of the sources of students' (often counterproductive) mathematical behavior.
Author: Jean-Michel Hoc
Publisher: Psychology Press
ISBN: 1134783655
Category : Psychology
Languages : en
Pages : 327
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Book Description
Technological development has changed the nature of industrial production so that it is no longer a question of humans working with a machine, but rather that a joint human machine system is performing the task. This development, which started in the 1940s, has become even more pronounced with the proliferation of computers and the invasion of digital technology in all wakes of working life. It may appear that the importance of human work has been reduced compared to what can be achieved by intelligent software systems, but in reality, the opposite is true: the more complex a system, the more vital the human operator's task. The conditions have changed, however, whereas people used to be in control of their own tasks, today they have become supervisors of tasks which are shared between humans and machines. A considerable effort has been devoted to the domain of administrative and clerical work and has led to the establishment of an internationally based human-computer interaction (HCI) community at research and application levels. The HCI community, however, has paid more attention to static environments where the human operator is in complete control of the situation, rather than to dynamic environments where changes may occur independent of human intervention and actions. This book's basic philosophy is the conviction that human operators remain the unchallenged experts even in the worst cases where their working conditions have been impoverished by senseless automation. They maintain this advantage due to their ability to learn and build up a high level of expertise -- a foundation of operational knowledge -- during their work. This expertise must be taken into account in the development of efficient human-machine systems, in the specification of training requirements, and in the identification of needs for specific computer support to human actions. Supporting this philosophy, this volume *deals with the main features of cognition in dynamic environments, combining issues coming from empirical approaches of human cognition and cognitive simulation, *addresses the question of the development of competence and expertise, and *proposes ways to take up the main challenge in this domain -- the design of an actual cooperation between human experts and computers of the next century.
Author:
Publisher: Academic Press
ISBN: 0080863809
Category : Computers
Languages : en
Pages : 573
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Book Description
The objective of the series has always been to provide a forum in which leading contributors to an area can write about significant bodies of research in which they are involved. The operating procedure has been to invite contributions from interesting, active investigators, and then allow them essentially free rein to present their perspectives on important research problems. The result of such invitations over the past two decades has been collections of papers which consist of thoughtful integrations providing an overview of a particular scientific problem. The series has an excellent tradition of high quality papers and is widely read by researchers in cognitive and experimental psychology.
