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Foundations of Knowledge Acquisition : Machine Learning :  Machine Learning - Susan F. Chipman

Foundations of Knowledge Acquisition : Machine Learning

Machine Learning

By: Susan F. Chipman (Editor), Alan L. Meyrowitz (Editor)

Hardcover Published: December 2009
ISBN: 9780792392781
Number Of Pages: 334

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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. This volume is subtitled Machine Learning, and there is a companion volume subtitled Cognitive Models of Complex Learning. Funding was provided by a five-year Accelerated Research Initiative (ARI), and made possible significant advances in the scientific understanding of how machines and humans can acquire new knowledge so as to exhibit improved problem-solving behavior. Significant progress in machine learning is reported along a variety of fronts. Chapters in Machine Learning include work in analogical reasoning; induction and discovery; learning and planning; learning by competition, using genetic algorithms; and theoretical limitations. Knowledge acquisition as pursued under the ARI was a coordinated research thrust into both machine learning and human learning. Chapters in Cognitive Modles of Complex Learning, also published by Kluwer Academic Publishers, include summaries of work by cognitive scientists who do computational modeling 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. These volumes of Foundations of Knowledge Acquisition serve as excellent reference sources by bringing together descriptions of recent and on-going research at the forefront of progress in one of the most challenging arenas of artificial intelligence and cognitive science. In addition, contributing authors comment on exciting future directions for research.

Foreword. Preface. 1. Learning = Inferencing + Memorizing; R.S. Michalski. 2. Adaptive Inference; A. Segre, C. Elkan, D. Scharstein, G. Gordon, A. Russell. 3. On Integrating Machine Learning with Planning; G.F. DeJong, M.T. Gervasio, S.W. Bennett. 4. The Role of Self-Models in Learning to Plan; G. Collins, L. Birnbaum, B. Krulwich, M. Freed. 5. Learning Flexible Concepts Using A Two-Tiered Representation; R.S. Michalski, F. Bergadano, S. Matwin., J. Zhang. 6. Competition-Based Learning; J.J. Grefenstette, K.A. De Jong, W.M. Spears. 7. Problem Solving via Analogical Retrieval and Analogical Search Control; R. Jones. 8. A View of Computational Learning Theory; L.G. Valiant. 9. The Probably Approximately Correct (PAC) and Other Learning Models; D. Haussler, M. Warmuth. 10. On the Automated Discovery of Scientific Theories; D. Osherson, S. Weinstein. Index.

ISBN: 9780792392781
ISBN-10: 0792392787
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 334
Published: December 2009
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 24.77 x 16.51  x 1.91
Weight (kg): 0.67

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