Foundations Of Knowledge Acquisition Machine Learning
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Foundations of Knowledge Acquisition by Alan L. Meyrowitz,Susan Chipman Pdf
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.
Foundations of Knowledge Acquisition by Susan Chipman,Alan L. Meyrowitz Pdf
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.
The Foundations of Knowledge Acquisition by Brian R. Gaines,John H. Boose Pdf
This book presents a broad view of the fundamental issues involved in knowledge acquisition and their place in knowledge-based systems development. The book covers theory based methods and problem modeling approaches to provide a strong theoretical and methodological basis for practical and effective knowledge acquisition techniques.
Knowledge Acquisition: Selected Research and Commentary by Sandra Marcus Pdf
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.
Knowledge Management and Acquisition for Smart Systems and Services by Yang Sok Kim,Byeong Ho Kang,Deborah Richards Pdf
This book constitutes the proceedings of the 13th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2014, held in Gold Cost, Qld, Australia, in December 2014. The 18 full papers and 4 short papers included in this volume were carefully reviewed and selected from 69 initial submissions. They deal with knowledge acquisition, expert systems, intelligent agents, ontology engineering, foundations of artificial intelligence, machine learning, data mining, Web mining, information systems, Web and other applications.
Machine Learning: Theoretical Foundations and Practical Applications by Manjusha Pandey,Siddharth Swarup Rautaray Pdf
This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9–12 January 2020 in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network learning, knowledge acquisition and learning, knowledge intensive learning, machine learning and information retrieval, machine learning for web navigation and mining, learning through mobile data mining, text and multimedia mining through machine learning, distributed and parallel learning algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory, cognitive modelling and hybrid learning algorithms.
Knowledge Acquisition for Expert Systems by A. Kidd Pdf
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.
Readings in Knowledge Acquisition and Learning by Bruce G. Buchanan,David C. Wilkins Pdf
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.
Concept Formation and Knowledge Revision by Stefan Wrobel Pdf
A fundamental assumption of work in artificial intelligence and machine learning is that knowledge is expressed in a computer with the help of knowledge representations. Since the proper choice of such representations is a difficult task that fundamentally affects the capabilities of a system, the problem of automatic representation change is an important topic in current research. Concept Formation and Knowledge Revision focuses on representation change as a concept formation task, regarding concepts as the elementary representational vocabulary from which further statements are constructed. Taking an interdisciplinary approach from psychological foundations to computer implementations, the book draws on existing psychological results about the nature of human concepts and concept formation to determine the scope of concept formation phenomena, and to identify potential components of computational concept formation models. The central idea of this work is that computational concept formation can usefully be understood as a process that is triggered in a demand-driven fashion by the representational needs of the learning system, and identify the knowledge revision activities of a system as a particular context for such a process. The book presents a detailed analysis of the revision problem for first-order clausal theories, and develops a set of postulates that any such operation should satisfy. It shows how a minimum theory revision operator can be realized by using exception sets, and that this operator is indeed maximally general. The book then shows that concept formation can be triggered from within the knowledge revision process whenever the existing representation does not permit the plausible reformulation of an exception set, demonstrating the usefulness of the approach both theoretically and empirically within the learning knowledge acquisition system MOBAL. In using a first-order representation, this book is part of the rapidly developing field of Inductive Logic Programming (ILP). By integrating the computational issues with psychological and fundamental discussions of concept formation phenomena, the book will be of interest to readers both theoretically and psychologically inclined. From the foreword by Katharina Morik: ` The ideal to combine the three sources of artificial intelligence research has almost never been reached. Such a combined and integrated research requires the researcher to master different ways of thinking, different work styles, different sets of literature, and different research procedures. It requires capabilities in software engineering for the application part, in theoretical computer science for the theory part, and in psychology for the cognitive part. The most important capability for artificial intelligence is to keep the integrative view and to create a true original work that goes beyond the collection of pieces from different fields. This book achieves such an integrative view of concept formation and knowledge revision by presenting the way from psychological investigations that indicate that concepts are theories and point at the important role of a demand for learning. to an implemented system which supports users in their tasks when working with a knowledge base and its theoretical foundation. '
Knowledge Acquisition and Machine Learning by Katharina Morik Pdf
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
Machine Learning and Knowledge Acquisition by Gheorghe Tecuci,Yves Kodratoff Pdf
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.
Knowledge Acquisition, Knowledge Programming, and Knowledge Refinement by Frederick Hayes-Roth,Philip Klahr,David J. Mostow,Rand Corporation Pdf
This report describes the principal findings and recommendations of a 2-year Rand research project on machine-aided knowledge acquisition and discusses the transfer of expertise from humans to machines, as well as the functions of planning, debugging, knowledge refinement, and autonomous machine learning. The relative advantages of humans and machines in the building of intelligent systems are explained. Background and guidance is provided for policymakers concerned with the research and development of machine-based learning systems. The research method adopted emphasized iterative refinement of knowledge in response to actual experience; i.e., a machine's knowledge was acquired initially from a human who provided enough concepts, constraints, and problem-solving heuristics to define some minimal level of performance. Sixty-two references are listed. (Author/FM)
Constructive Knowledge Acquisition by Franz Schmalhofer Pdf
A cognitive psychology which becomes increasingly specialized requires a special effort in order to avoid a fragmentation into several controversial issues that are independently discussed but also inherently related. Rather than asking additional differentiated questions which are then investigated by more specialized experimental methods and designs, this book promotes unified theories and a levels approach for their experimental evaluation. Within this cognitive science approach and on the basis of the most foundational assumptions of Kintsch's construction integration theory, a computational theory of knowledge acquisition is then developed and subsequently evaluated by psychological experiments. For forty years, computer simulation techniques and experimental psychology research have greatly matured the understanding of human knowledge and its acquisition in different learning environments. This volume critically assesses the advantages and limitations of these approaches and then develops an integrated research methodology. It goes on to provide significant progress concerning the following questions: * What are the most promising research methodologies for investigating human cognition? * How can the experimental psychology research on text comprehension, concept formation, and memory become more closely related to one another when the very specialized research paradigms and the highly specific scientific controversies promote their separation and independent discussion? * How can a general comprehension-based theory bridge the gap between simple experimental settings and the real-life situations that occur in education and work environments? This book demonstrates how experimental psychology can proceed more successfully by investigating those aspects that are shared among different areas of research like text comprehension, categorization, and learning by exploration. It also shows how unified theories can assist in applying experimental psychology and cognitive science results to areas such as intelligent tutoring systems, instructional design, and the development of expert systems in complex real world domains.