Foundations of Knowledge Acquisition : Cognitive Models of Complex Learning

Susan Chipman

ISBN 10: 0792392779 / ISBN 13: 9780792392774

Published by Springer Us Dez 1992, 1992

About this Item

Neuware - One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact ofsuccessful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain aboutthe methods by which machines and humans might learn, significant progress has been made.

Seller Inventory # 9780792392774

Bibliographic Details

• Title: Foundations of Knowledge Acquisition : Cognitive Models of Complex Learning
• Publisher: Springer Us Dez 1992
• Publication Date: 1992
• Language: English
• ISBN 10: 0792392779
• ISBN 13: 9780792392774
• Binding: Buch
• Condition: Neu

About this title

Synopsis

One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact ofsuccessful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain aboutthe methods by which machines and humans might learn, significant progress has been made.

Synopsis

The two volumes of "Foundations of Knowledge Acquisition" document the recent progress of basic research in knowledge acquisition sponsored by the Office of Naval Research. Knowledge acquisition as pursued under the ARI was a coordinated research thrust into both machine learning and human learning. Chapters in this volume include summaries of work by cognitive scientists who do computational modelling of human learning. In fact, an accomplishment of research previously sponsored by ONR's Cognitive Science Program was insight into the knowledge and skills that distinguish human novices from human experts in various domains; the cognitive interest in the ARI was then to characterize how the transition from novice to expert actually takes place. In the accompanying volume, "Machine Learning", significant progress in machine learning is reported along a variety of fronts. Chapters include work in analogical reasoning; induction and discovery; learning and planning; learning by competition, using genetic algorithms; and theoretical limitations.