[PDF] Machine Learning And Knowledge Acquisition eBook

Author : Sandra Marcus
Publisher : Springer Science & Business Media
Page : 150 pages
File Size : 40,66 MB
Release : 2012-12-06
Category : Computers
ISBN : 146131531X

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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.

Author : Alan L. Meyrowitz
Publisher : Springer Science & Business Media
Page : 341 pages
File Size : 10,29 MB
Release : 2007-08-19
Category : Computers
ISBN : 0585273669

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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.

Author : Gheorghe Tecuci
Publisher :
Page : 344 pages
File Size : 46,71 MB
Release : 1995
Category : Business & Economics
ISBN :

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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.

Author : Bob Wielinga
Publisher : IOS Press
Page : 390 pages
File Size : 38,11 MB
Release : 1990
Category : Computers
ISBN : 9789051990362

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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.

Author : Susan Chipman
Publisher : Springer Science & Business Media
Page : 347 pages
File Size : 17,65 MB
Release : 2012-12-06
Category : Computers
ISBN : 1461531721

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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.

Author : Bruce G. Buchanan
Publisher : Morgan Kaufmann Publishers
Page : 926 pages
File Size : 17,84 MB
Release : 1993
Category : Computers
ISBN :

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Readings in Knowledge Acquisition and Learning collects the best of the artificial intelligence literature from the fields of machine learning and knowledge acquisition. This book brings together the perspectives on constructing knowledge-based systems from these two historically separate subfields of artificial intelligence.

Author : Ray Bareiss
Publisher : Academic Press
Page : 184 pages
File Size : 47,54 MB
Release : 2014-05-10
Category : Computers
ISBN : 1483216373

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Exemplar-Based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning covers the fundamental issues in cognitive science and the technology for solving real problems. This text contains six chapters and begins with a description of the rationale for the design of Protos Approach, its construction and performance. The succeeding chapters discuss how the Protos approach meets the requirements of representing concepts, using them for classification, and acquiring them from available training. These chapters also deal with the design and implementation of Protos. These topics are followed by a presentation of examples of the application of Protos to audiology and evaluate its performance. The final chapters survey related work in the areas of case-based reasoning and automated knowledge acquisition and the contributions of Protos approach. This book will be of great value to psychologists, psychiatrists, and researchers in the field of artificial intelligence.

Author : Katharina Morik
Publisher : Academic Press
Page : 344 pages
File Size : 10,3 MB
Release : 1993-09-13
Category : Computers
ISBN :

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For graduate-/research- level students and professors, this book integrates machine learning with knowledge acquisition to overcome the problems of building models for knowledge-based systems to maintain them successfully. It also reports on BLIP and MOBAL systems developed over the last decade, which illustrate a particular way of unifying knowledge acquisition and machine learning. Practically-orientated, theoretical skills have been used and tested in real-world applications. Integrates machine learning with knowledge acquisition to overcome the problems of building models for knowledge based systems to maintain them successfully Reports on BLIP and MOBAL systems that have been developed over the past 10 years, which illustrate a particular way of unifying knowledge acquisition and machine learning Practically oriented--theoretical results have been used and tested in real-world applications from the start

Author : A. Kidd
Publisher : Springer Science & Business Media
Page : 203 pages
File Size : 22,20 MB
Release : 2012-12-06
Category : Psychology
ISBN : 1461318238

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Building an expert system involves eliciting, analyzing, and interpreting the knowledge that a human expert uses when solving problems. Expe rience has shown that this process of "knowledge acquisition" is both difficult and time consuming and is often a major bottleneck in the production of expert systems. Unfortunately, an adequate theoretical basis for knowledge acquisition has not yet been established. This re quires a classification of knowledge domains and problem-solving tasks and an improved understanding of the relationship between knowledge structures in human and machine. In the meantime, expert system builders need access to information about the techniques currently being employed and their effectiveness in different applications. The aim of this book, therefore, is to draw on the experience of AI scientists, cognitive psychologists, and knowledge engineers in discussing particular acquisition techniques and providing practical advice on their application. Each chapter provides a detailed description of a particular technique or methodology applied within a selected task domain. The relative strengths and weaknesses of the tech nique are summarized at the end of each chapter with some suggested guidelines for its use. We hope that this book will not only serve as a practical handbook for expert system builders, but also be of interest to AI and cognitive scientists who are seeking to develop a theory of knowledge acquisition for expert systems.