Representing Uncertain Knowledge

Representing Uncertain Knowledge

Author: Paul Krause

Publisher: Springer Science & Business Media

ISBN: 9789401120845

Category: Computers

Page: 277

View: 651

The representation of uncertainty is a central issue in Artificial Intelligence (AI) and is being addressed in many different ways. Each approach has its proponents, and each has had its detractors. However, there is now an in creasing move towards the belief that an eclectic approach is required to represent and reason under the many facets of uncertainty. We believe that the time is ripe for a wide ranging, yet accessible, survey of the main for malisms. In this book, we offer a broad perspective on uncertainty and approach es to managing uncertainty. Rather than provide a daunting mass of techni cal detail, we have focused on the foundations and intuitions behind the various schools. The aim has been to present in one volume an overview of the major issues and decisions to be made in representing uncertain knowl edge. We identify the central role of managing uncertainty to AI and Expert Systems, and provide a comprehensive introduction to the different aspects of uncertainty. We then describe the rationales, advantages and limitations of the major approaches that have been taken, using illustrative examples. The book ends with a review of the lessons learned and current research di rections in the field. The intended readership will include researchers and practitioners in volved in the design and implementation of Decision Support Systems, Ex pert Systems, other Knowledge-Based Systems and in Cognitive Science.
Representing Uncertain Knowledge
Language: en
Pages: 277
Authors: Paul Krause, Dominic Clark
Categories: Computers
Type: BOOK - Published: 2012-12-06 - Publisher: Springer Science & Business Media

The representation of uncertainty is a central issue in Artificial Intelligence (AI) and is being addressed in many different ways. Each approach has its proponents, and each has had its detractors. However, there is now an in creasing move towards the belief that an eclectic approach is required to represent
Artificial Intelligence
Language: en
Pages: 477
Authors: Ela Kumar
Categories: Computers
Type: BOOK - Published: 2013-12-30 - Publisher: I. K. International Pvt Ltd

AI is an emerging discipline of computer science. It deals with the concepts and methodologies required for computer to perform an intelligent activity. The spectrum of computer science is very wide and it enables the computer to handle almost every activity, which human beings could. It deals with defining the
Uncertainty in Artificial Intelligence
Language: en
Pages: 378
Authors: Didier J. Dubois, Michael P. Wellman, Bruce D'Ambrosio
Categories: Computers
Type: BOOK - Published: 2014-05-12 - Publisher: Morgan Kaufmann

Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (1992) covers the papers presented at the Eighth Conference on Uncertainty in Artificial Intelligence, held at Stanford University on July 17-19, 1992. The book focuses on the processes, methodologies, technologies, and approaches involved in artificial intelligence. The selection first offers information
Quantified Representation of Uncertainty and Imprecision
Language: en
Pages: 477
Authors: Dov M. Gabbay, Philippe Smets
Categories: Philosophy
Type: BOOK - Published: 2013-11-11 - Publisher: Springer Science & Business Media

We are happy to present the first volume of the Handbook of Defeasible Reasoning and Uncertainty Management Systems. Uncertainty pervades the real world and must therefore be addressed by every system that attempts to represent reality. The representation of uncertainty is a ma jor concern of philosophers, logicians, artificial intelligence
Uncertainty Reasoning for the Semantic Web II
Language: en
Pages: 331
Authors: Fernando Bobillo, Paulo Cesar G. Costa, Claudia d'Amato, Nicola Fanizzi, Kathryn B. Laskey, Kenneth J. Laskey, Thomas Lukasiewicz, Matthias Nickles, Michael Pool
Categories: Computers
Type: BOOK - Published: 2013-01-09 - Publisher: Springer

This book contains revised and significantly extended versions of selected papers from three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2008, 2009, and 2010 or presented at the first international Workshop on Uncertainty in Description Logics (UniDL), held at