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Physica Changes of Problem Representation: Theory and Experiments - AI

Amazon

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Description

This academic volume explores how automating representation changes enhances artificial intelligence problem-solving efficiency. The research demonstrates that identical problems yield vastly different difficulty levels depending on their description and the search algorithm applied. The book documents a developed system that automatically improves problem input descriptions and selects optimal search algorithms tailored to each specific problem. Drawing on accumulated evidence about representation's critical role in AI system performance, this work advances understanding of how computational efficiency improves through strategic problem reformulation. Published by Physica-Verlag as part of the Studies in Fuzziness and Soft Computing series (volume 110), it provides both theoretical foundations and experimental validation for representation-focused AI enhancement.

Key Features

Focuses on automating representation changes to boost artificial intelligence problem-solving efficiency and speed.

Demonstrates how the same problem becomes easy or difficult based on description approach and algorithm selection.

Presents a developed system that automatically improves problem descriptions and matches appropriate search algorithms.

Part of Studies in Fuzziness and Soft Computing series volume 110 from Physica-Verlag.

Combines theoretical research with experimental evidence on AI representation and computational problem-solving.

Product Specifications

Brand
Physica
Format
hardcover
Domain
Amazon UK
Release Date
22 October 2002
Listed Since
15 January 2007

Barcode

No barcode data available

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