Knowledge Acquisition: Selected Research and Commentary - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Knowledge Acquisition: Selected Research and Commentary write by Sandra Marcus. This book was released on 2012-12-06. Knowledge Acquisition: Selected Research and Commentary available in PDF, EPUB and Kindle. What follows is a sampler of work in knowledge acquisition. It comprises three technical papers and six guest editorials. The technical papers give an in-depth look at some of the important issues and current approaches in knowledge acquisition. The editorials were pro duced by authors who were basically invited to sound off. I've tried to group and order the contributions somewhat coherently. The following annotations emphasize the connections among the separate pieces. Buchanan's editorial starts on the theme of "Can machine learning offer anything to expert systems?" He emphasizes the practical goals of knowledge acquisition and the challenge of aiming for them. Lenat's editorial briefly describes experience in the development of CYC that straddles both fields. He outlines a two-phase development that relies on an engineering approach early on and aims for a crossover to more automated techniques as the size of the knowledge base increases. Bareiss, Porter, and Murray give the first technical paper. It comes from a laboratory of machine learning researchers who have taken an interest in supporting the development of knowledge bases, with an emphasis on how development changes with the growth of the knowledge base. The paper describes two systems. The first, Protos, adjusts the training it expects and the assistance it provides as its knowledge grows. The second, KI, is a system that helps integrate knowledge into an already very large knowledge base.
Foundations of Knowledge Acquisition: Machine learning
Foundations of Knowledge Acquisition: Machine learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Foundations of Knowledge Acquisition: Machine learning write by Susan F. Chipman. This book was released on 1993. Foundations of Knowledge Acquisition: Machine learning available in PDF, EPUB and Kindle.
Foundations of Knowledge Acquisition
Foundations of Knowledge Acquisition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Foundations of Knowledge Acquisition write by Alan L. Meyrowitz. This book was released on 2007-08-19. Foundations of Knowledge Acquisition available in PDF, EPUB and Kindle. 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.
Machine Learning and Knowledge Acquisition
Machine Learning and Knowledge Acquisition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning and Knowledge Acquisition write by Gheorghe Tecuci. This book was released on 1995. Machine Learning and Knowledge Acquisition available in PDF, EPUB and Kindle. Currently, both fields are moving towards an integrated approach using machine learning techniques to automate knowledge acquisition from experts, and knowledge acquisition techniques to guide and assist the learning process.
Current Trends in Knowledge Acquisition
Current Trends in Knowledge Acquisition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Current Trends in Knowledge Acquisition write by Bob Wielinga. This book was released on 1990. Current Trends in Knowledge Acquisition available in PDF, EPUB and Kindle. Knowledge acquisition has become a major area of artificial intelligence and cognitive science research. The papers in this book show that the area of knowledge acquisition for knowledge-based systems is still a diverse field in which a large number of research topics are being addressed. However, several main themes run through the papers. First, the issues of integrating knowledge from different sources and K.A. tools is a salient topic in many papers. A second major topic in the papers is that of knowledge modelling. Research in knowledge-based systems emphasises the use of generic models of reasoning and its underlying knowledge. An important trend in the area of knowledge modelling aims at the formalisation of knowledge models. Where the field of knowledge acquisition was without tools and techniques years ago, now there is a rapidly growing body of techniques and tools. Apart from the integrated workbenches already mentioned above, several papers in this book present new tools. Although knowledge acquisition and machine learning have been considered as separate subfields of AI, there is a tendency for the two fields to come together. This publication combines machine learning techniques with more conventional knowledge elicitation techniques. A framework is presented in which reasoning, problem solving and learning together form a knowledge intensive system that can acquire knowledge from its own experience.
