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Springer Representing Uncertain Knowledge: AI Approach - Springer

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Description

This comprehensive guide from Springer explores the central challenge of representing uncertainty in Artificial Intelligence through multiple proven methodologies. The book surveys leading formalisms and approaches for managing uncertainty across diverse AI applications, offering both breadth and accessibility to practitioners and researchers. Rather than overwhelming readers with disconnected techniques, this work presents an eclectic yet integrated perspective on uncertainty representation, demonstrating how different methods complement each other when addressing real-world AI problems. Whether you work with probabilistic reasoning, fuzzy logic, belief systems, or other uncertainty frameworks, this resource bridges theoretical foundations with practical implementation considerations. Ideal for computer scientists, AI researchers, and machine learning professionals seeking deeper understanding of how to model and reason under uncertainty.

Key Features

Springer publication offering authoritative coverage of uncertainty representation methods in Artificial Intelligence applications.

Broad survey of main formalisms for handling uncertainty, making complex concepts accessible to both students and experienced practitioners.

Eclectic approach integrating multiple methodologies rather than advocating for single uncertainty management technique.

Comprehensive examination of how different approaches to uncertainty reasoning address distinct facets of AI problem-solving.

Product Specifications

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

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No barcode data available

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