Automated Generation of Model-based Knowledge-acquisition Tools
Author: Mark A. Musen
Publisher: Morgan Kaufmann
Published: 1989
Total Pages: 318
ISBN-13:
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Automated Generation of Adaptable Knowledge-acquisition Tools with Mecano
Author: Stanford University. Computer Science Department. Knowledge Systems Laboratory
Publisher:
Published: 1991
Total Pages: 9
ISBN-13:
DOWNLOAD EBOOK →Automating Knowledge Acquisition for Expert Systems
Author: Sandra Marcus
Publisher: Springer Science & Business Media
Published: 2013-03-08
Total Pages: 282
ISBN-13: 1468471228
DOWNLOAD EBOOK →In June of 1983, our expert systems research group at Carnegie Mellon University began to work actively on automating knowledge acquisition for expert systems. In the last five years, we have developed several tools under the pressure and influence of building expert systems for business and industry. These tools include the five described in chapters 2 through 6 - MORE, MOLE, SALT, KNACK and SIZZLE. One experiment, conducted jointly by developers at Digital Equipment Corporation, the Soar research group at Carnegie Mellon, and members of our group, explored automation of knowledge acquisition and code development for XCON (also known as R1), a production-level expert system for configuring DEC computer systems. This work influenced the development of RIME, a programming methodology developed at Digital which is the subject of chapter 7. This book describes the principles that guided our work, looks in detail at the design and operation of each tool or methodology, and reports some lessons learned from the enterprise. of the work, brought out in the introductory chapter, is A common theme that much power can be gained by understanding the roles that domain knowledge plays in problem solving. Each tool can exploit such an understanding because it focuses on a well defined problem-solving method used by the expert systems it builds. Each tool chapter describes the basic problem-solving method assumed by the tool and the leverage provided by committing to the method.
Automated generation of model based knowledge acquisiton tools
A Future for Knowledge Acquisition
Author: Luc Steels
Publisher: Springer Science & Business Media
Published: 1994-09-14
Total Pages: 438
ISBN-13: 9783540584872
DOWNLOAD EBOOK →In the last few years rapid advances have been made in reproductive medicine, making it necessary for those involved to regularly update their knowledge. The purpose of this book is to describe the state of the art in this field, making it possible for the reader to gain an orientation among all the diagnostic and therapeutic potentials of modern reproductive medicine in order to advise patients fully. Chapters from the fields of gynecology, and reproductive medicine in a specific sense provide knowledge about these subjects. Authors of international standing have contributed chapters on their specialties. These chapters together form a book describing the state of the art in the diagnosis and therapy of sterility in gynecology and andrology.
KADS
Author: Guus Schreiber
Publisher: Academic Press
Published: 1993-05-05
Total Pages: 484
ISBN-13: 9780126290400
DOWNLOAD EBOOK →KADS is a structured methodology for the development of knowledge based systems which has been adopted throughout the world by academic and industrial professionals alike. KADS approaches development as a modeling activity. Two key characteristics of KADS are the use of multiple models to cope with the complexity of knowledge engineering and the use of knowledge-level descriptions as an immediate model between system design and expertise data. The result is that KADS enables effective KBS construction by building a computational model of desired behavior for a particular problem domain. KADS contains three section: the Theoretical Basis of KADS, Languages and Tools, and Applications. Together they form a comprehensive sourcebook of the how and why of the KADS methodology. KADS will be required reading for all academic and industrial professionals concerned with building knowledge-based systems. It will also be a valuable source for students of knowledge acquisition and KBS. * SPECIAL FEATURES: * KADS is the most widely used commercial structured methodology for KBS development in Europe and is becoming one of the few significant AI exports to the US. * Describes KADS from its Theoretical Basis, through Language and Tool Developments, to real Applications.
Current Trends in Knowledge Acquisition
Author: Bob Wielinga
Publisher: IOS Press
Published: 1990
Total Pages: 390
ISBN-13: 9789051990362
DOWNLOAD EBOOK →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.
Knowledge Acquisition, Modeling and Management
Author: Rudi Studer
Publisher: Springer
Published: 2003-06-29
Total Pages: 413
ISBN-13: 3540487751
DOWNLOAD EBOOK →Past, Present, and Future of Knowledge Acquisition This book contains the proceedings of the 11th European Workshop on Kno- edge Acquisition, Modeling, and Management (EKAW ’99), held at Dagstuhl Castle (Germany) in May of 1999. This continuity and the high number of s- missions re?ect the mature status of the knowledge acquisition community. Knowledge Acquisition started as an attempt to solve the main bottleneck in developing expert systems (now called knowledge-based systems): Acquiring knowledgefromahumanexpert. Variousmethodsandtoolshavebeendeveloped to improve this process. These approaches signi?cantly reduced the cost of - veloping knowledge-based systems. However, these systems often only partially ful?lled the taskthey weredevelopedfor andmaintenanceremainedanunsolved problem. This required a paradigm shift that views the development process of knowledge-based systems as a modeling activity. Instead of simply transf- ring human knowledge into machine-readable code, building a knowledge-based system is now viewed as a modeling activity. A so-called knowledge model is constructed in interaction with users and experts. This model need not nec- sarily re?ect the already available human expertise. Instead it should provide a knowledgelevelcharacterizationof the knowledgethat is requiredby the system to solve the application task. Economy and quality in system development and maintainability are achieved by reusable problem-solving methods and onto- gies. The former describe the reasoning process of the knowledge-based system (i. e. , the algorithms it uses) and the latter describe the knowledge structures it uses (i. e. , the data structures). Both abstract from speci?c application and domain speci?c circumstances to enable knowledge reuse.
Knowledge Acquisition, Modeling and Management
Author: Dieter Fensel
Publisher: Springer Science & Business Media
Published: 1999-05-19
Total Pages: 413
ISBN-13: 3540660445
DOWNLOAD EBOOK →This book constitutes the refereed proceedings of the 11th European Workshop on Knowledge Acquisition, Modeling and Management, EKAW '99, held at Dagstuhl Castle, Germany in May 1999. The volume presents 16 revised full papers and 15 revised short papers were carefully reviewed and selected form a high number of submissions. Also included are two invited papers. The papers address issues of knowledge acquisition (i.e., the process of extracting, creating, structuring knowledge, etc.), of knowledge-level modeling for knowledge-based systems, and of applying and redefining this work in a knowledge management and knowledge engineering context.
Knowledge Acquisition: Selected Research and Commentary
Author: Sandra Marcus
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 150
ISBN-13: 146131531X
DOWNLOAD EBOOK →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.
