Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning (The Springer International Series in Engineering and Computer Science, 194)

ISBN 10: 0792392779 / ISBN 13: 9780792392774

Published by Springer, 1992

About this Item

Good: A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, highlighting of text, writing in margins. No missing pages.

Seller Inventory # mon0000004963

Bibliographic Details

• Title: Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning (The Springer International Series in Engineering and Computer Science, 194)
• Publisher: Springer
• Publication Date: 1992
• Language: English
• ISBN 10: 0792392779
• ISBN 13: 9780792392774
• Binding: hardcover
• Condition: Very Good
• Dust Jacket Condition: Dust Jacket Included

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.