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Springer - Representing Uncertain Knowledge: AI Approach Book

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Description

The representation of uncertainty stands as a central issue within the field of Artificial Intelligence. As different methodologies emerge, many experts believe that an eclectic approach is necessary to effectively represent and reason through the various facets of uncertainty. This book provides a wide ranging and accessible survey of the main formalisms used in the industry today. Rather than presenting a daunting mass of technical data, this work offers a broad perspective on how to manage uncertainty. It serves as a comprehensive guide for those looking to understand the different ways uncertainty is addressed in AI. By examining various approaches and their respective proponents, this text helps readers navigate the complex landscape of AI reasoning and management.

Key Features

Provides a broad perspective on the many different ways uncertainty is addressed within Artificial Intelligence.

Offers a wide ranging yet accessible survey of the main formalisms used to represent uncertain knowledge.

Explores the need for an eclectic approach to effectively reason under many different facets of uncertainty.

Delivers a comprehensive overview of the various approaches and methodologies used in AI research.

Designed to provide a clear understanding of managing uncertainty without becoming a daunting mass of information.

Product Specifications

Format
paperback
Domain
Amazon UK
Publication Date
06 November 2012
Listed Since
03 March 2013

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