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 of successful 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 about the methods by which machines and humans might learn, significant progress has been made.

Klappentext

The two volumes of emFoundations of Knowledge Acquisition/em document the recent progress of basic research in knowledge acquisition sponsored by the Office of Naval Research. This volume is subtitled emMachine/em emLearning/em, and there is a companion volume subtitled emCognitive Models/em emof Complex Learning/em. 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 emMachine Learning/em 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 emCognitive Modles of Complex Learning/em, 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 emFoundations of Knowledge Acquisition/em 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.

Inhalt
Learning = Inferencing + Memorizing.- Adaptive Inference.- On Integrating Machine Learning with Planning.- The Role of Self-Models in Learning to Plan.- Learning Flexible Concepts Using a Two-Tiered Representation.- Competition-Based Learning.- Problem Solving via Analogical Retrieval and Analogical Search Control.- A View of Computational Learning Theory.- The Probably Approximately Correct (PAC) and Other Learning Models.- On the Automated Discovery of Scientific Theories.