Exemplar-Based Knowledge Acquisition


Book Description

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.




Knowledge Acquisition: Selected Research and Commentary


Book Description

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.




Contemporary Knowledge Engineering and Cognition


Book Description

This book has its source in the question of whether any knowledge engineering tools can be applied or analyzed in cognition research and what insights and methods of cognitive science might be relevant for knowledge engineers. It presents the proceedings of a workshop organized by the Special Interest Groups Cognition and Knowledge Engineering of the German Society for Informatics, held in February 1992 in Kaiserslautern. The book is structured into three parts. The first part contrasts work in knowledge engineering with approaches from the side of the "soft sciences". The second part deals with case-based approaches in expert systems. Cognition research and the cognitive adequacy of expert systems are discussed in the third part. Contributions from Canada, England, France, Switzerland, and the USA demonstrate how knowledge engineering and cognitive science are woven together internationally.




Foundations of Knowledge Acquisition


Book Description

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.




The Acquisition of Strategic Knowledge


Book Description

The Acquisition of Strategic Knowledge deals with the automation of the acquisition of strategic knowledge and describes a knowledge acquisition program called ASK, which elicits strategic knowledge from domain experts and puts it in operational form. This book explores the dynamics of intelligent systems and how the components of knowledge systems (including a human expert) interact to produce intelligence. Emphasis is placed on how to represent knowledge that experts require to make decisions about actions. The move toward abstract tasks and how tasks are solved are discussed, along with their implications for knowledge acquisition, particularly the acquisition of expert strategies. This book is comprised of eight chapters and begins with an overview of the knowledge acquisition problem for strategic knowledge, as well as the relevance of strategic knowledge to artificial intelligence. The next chapter describes a dialog session between the ASK knowledge acquisition assistant and the user (""the expert""). The discussion then turns to software architecture with which to represent strategic knowledge; design and implementation of an assistant for acquiring strategic knowledge; and approaches to knowledge acquisition. Two applications of the ASK system are considered: to evaluate the usability of the elicitation technique with real users and to test the adequacy of the strategy rule representation upon which the approach is dependent. The scope of ASK, its sources of power, and its underlying assumptions are also outlined. This monograph will be a valuable resource for knowledge systems designers and those interested in artificial intelligence and expert systems.




Current Trends in Knowledge Acquisition


Book Description

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.




The Psychology of Expertise


Book Description

This volume investigates our ability to capture, and then apply, expertise. In recent years, expertise has come to be regarded as an increasingly valuable and surprisingly elusive resource. Experts, who were the sole active dispensers of certain kinds of knowledge in the days before AI, have themselves become the objects of empirical inquiry, in which their knowledge is elicited and studied -- by knowledge engineers, experimental psychologists, applied psychologists, or other experts -- involved in the development of expert systems. This book achieves a marriage between experimentalists, applied scientists, and theoreticians who deal with expertise. It envisions the benefits to society of an advanced technology for capturing and disseminating the knowledge and skills of the best corporate managers, the most seasoned pilots, and the most renowned medical diagnosticians. This book should be of interest to psychologists as well as to knowledge engineers who are "out in the trenches" developing expert systems, and anyone pondering the nature of expertise and the question of how it can be elicited and studied scientifically. The book's scope and the pivotal concepts that it elucidates and appraises, as well as the extensive categorized bibliographies it includes, make this volume a landmark in the field of expert systems and AI as well as the field of applied experimental psychology.




Current Issues in Knowledge Management


Book Description

"This book combines research on the cultural, technical, organizational, and human issues surrounding the creation, capture, transfer, and use of knowledge in today's organizations. Topics such as organizational memory, knowledge management in enterprises, enablers and inhibitors of knowledge sharing and transfer, and emerging technologies of knowledge management, offering information to practitioners and scholars in a variety of settings"--Provided by publisher.




Automated Reasoning


Book Description

These essays have been written to honor W. W. Bledsoe, a scientist who has contributed to such diverse fields as mathematics, systems analysis, pattern recognition, biology, artificial intelligence, and automated reasoning. The first essay provides a sketch of his life, emphasizing his scientific contributions. The diversity of the fields to which Bledsoe has contributed is reflected in the range of the other essays, which are original scientific contributions by some of his many friends and colleagues. Bledsoe is a founding father of the field of automated reasoning, and a majority of the essays are on that topic. These essays are collected together here not only to acknowledge Bledsoe's manifold and substantial scientific contributions but also to express our appreciation for the great care and energy that he has devoted to nurturing many of the scientists working in those scientific fields he has helped found. Robert S. Boyer Austin February, 1991 ix Acknow ledgements Thanks to Larry Wos, editor of the Journal of Automated Reasoning, and Derek Middleton and Martin Scrivener, Kluwer Academic editors, for sup porting the idea of initiating this collection of essays. Thanks to A. Michael Ballantyne and Michael Spivak, for help with lffi.TWC, especially in identifying many formatting problems and providing fixes.




Simulation-Based Experiential Learning


Book Description

In October of 1992 an assembly of researchers in simulation and computer models for instruction convened in Bonas, France, to learn from one another in a non-automated environment. The event was the Advanced Research Workshop entitled The Use of Computer Models for Explication, Analysis, and Experiential Learning. Sponsored by the Scientific Affairs Division of NATO, this workshop brought together 29 leading experts in the field loosely described as instruction and learning in simulation environments. The three-day workshop was organized in a manner to maximize exchange of knowledge, of beliefs, and of issues. The participants came from six countries with experiences to share, with opinions to voice, and with questions to explore. Starting some weeks prior to the workshop, the exchange included presentation of the scientific papers, discussions immediately following each presentation, and informal discussions outside the scheduled meeting times. Naturally, the character and content of the workshop was determined by the backgrounds and interests of the participants. One objective in drawing together these particular specialists was to achieve a congress with coherent diversity, i.e., we sought individuals who could view an emerging area from different perspectives yet had produced work of interest to many. Major topic areas included theories of instruction being developed or tested, use of multiple domain models to enhance understanding, experiential learning environments, modelling diagnostic environments, tools for authoring complex models, and case studies from industry.