Knowledge Acquisition: Selected Research and Commentary
Knowledge Acquisition: Selected Research and Commentary

Author: Sandra Marcus

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 150

ISBN-13: 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.

Knowledge-Based Systems, Four-Volume Set
Knowledge-Based Systems, Four-Volume Set

Author: Cornelius T. Leondes

Publisher: Elsevier

Published: 2000-07-11

Total Pages: 1554

ISBN-13: 0080535283

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The design of knowledge systems is finding myriad applications from corporate databases to general decision support in areas as diverse as engineering, manufacturing and other industrial processes, medicine, business, and economics. In engineering, for example, knowledge bases can be utilized for reliable electric power system operation. In medicine they support complex diagnoses, while in business they inform the process of strategic planning. Programmed securities trading and the defeat of chess champion Kasparov by IBM's Big Blue are two familiar examples of dedicated knowledge bases in combination with an expert system for decision-making.With volumes covering "Implementation," "Optimization," "Computer Techniques," and "Systems and Applications," this comprehensive set constitutes a unique reference source for students, practitioners, and researchers in computer science, engineering, and the broad range of applications areas for knowledge-based systems.

Advances In Industrial Ergonomics And Safety IV
Advances In Industrial Ergonomics And Safety IV

Author: Shrawan Kumar

Publisher: CRC Press

Published: 2003-09-02

Total Pages: 1597

ISBN-13: 0203221338

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Topics Include: industrial ergonomics, risk, accidents and accident prevention, safety and surveillance, posture perception, cognitive ergonomics, telerobotics, military occupational ergonomics, and international ergonomics.

Foundations of Soft Case-Based Reasoning
Foundations of Soft Case-Based Reasoning

Author: Sankar K. Pal

Publisher: John Wiley & Sons

Published: 2004-07-01

Total Pages: 300

ISBN-13: 0471644668

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Provides a self-contained description of this important aspect of information processing and decision support technology. Presents basic definitions, principles, applications, and a detailed bibliography. Covers a range of real-world examples including control, data mining, and pattern recognition.

Knowledge Acquisition, Modeling and Management
Knowledge Acquisition, Modeling and Management

Author: Rudi Studer

Publisher: Springer

Published: 2003-06-29

Total Pages: 413

ISBN-13: 3540487751

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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.

Recent Developments in Decision Support Systems
Recent Developments in Decision Support Systems

Author: Clyde W. Holsapple

Publisher: Springer Science & Business Media

Published: 2013-06-29

Total Pages: 613

ISBN-13: 3662029529

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Over the past two decades, many advances have been made in the decision support system (DSS) field. They range from progress in fundamental concepts, to improved techniques and methods, to widespread use of commercial software for DSS development. Still, the depth and breadth of the DSS field continues to grow, fueled by the need to better support decision making in a world that is increasingly complex in terms of volume, diversity, and interconnectedness of the knowledge on which decisions can be based. This continuing growth is facilitated by increasing computer power and decreasing per-unit computing costs. But, it is spearheaded by the multifaceted efforts of DSS researchers. The collective work of these researchers runs from the speculative to the normative to the descriptive. It includes analysis of what the field needs, designs of means for meeting recognized needs, and implementations for study. It encompasses theoretical, empirical, and applied orientations. It is concerned with the invention of concepts, frameworks, models, and languages for giving varied, helpful perspectives. It involves the discovery of principles, methods, and techniques for expeditious construction of successful DSSs. It aims to create computer-based tools that facilitate DSS development. It assesses DSS efficacy by observing systems, their developers, and their users. This growing body of research continues to be fleshed out and take shape on a strong, but still-developing, skeletal foundation.