Changes of Problem Representation : Theory and Experiments

Eugene Fink

ISBN 10: 3790825182 / ISBN 13: 9783790825183

Published by Physica-Verlag HD, 2010

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - The purpose of our research is to enhance the efficiency of AI problem solvers by automating representation changes. We have developed a system that improves the description of input problems and selects an appropriate search algorithm for each given problem. Motivation. Researchers have accumulated much evidence on the impor tance of appropriate representations for the efficiency of AI systems. The same problem may be easy or difficult, depending on the way we describe it and on the search algorithm we use. Previous work on the automatic im provement of problem descriptions has mostly been limited to the design of individual learning algorithms. The user has traditionally been responsible for the choice of algorithms appropriate for a given problem. We present a system that integrates multiple description-changing and problem-solving algorithms. The purpose of the reported work is to formalize the concept of representation and to confirm the following hypothesis: An effective representation-changing system can be built from three parts: - a library of problem-solving algorithms; - a library of algorithms that improve problem descriptions; - a control module that selects algorithms for each given problem.

Seller Inventory # 9783790825183

Bibliographic Details

• Title: Changes of Problem Representation : Theory and Experiments
• Publisher: Physica-Verlag HD
• Publication Date: 2010
• Language: English
• ISBN 10: 3790825182
• ISBN 13: 9783790825183
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 21D millimeters 235 millimeters width by 155 millimeters height by 21 millimeters depth
• Weight: 563 grams

About this title

Synopsis

The purpose of our research is to enhance the efficiency of AI problem solvers by automating representation changes. We have developed a system that improves the description of input problems and selects an appropriate search algorithm for each given problem. Motivation. Researchers have accumulated much evidence on the impor­ tance of appropriate representations for the efficiency of AI systems. The same problem may be easy or difficult, depending on the way we describe it and on the search algorithm we use. Previous work on the automatic im­ provement of problem descriptions has mostly been limited to the design of individual learning algorithms. The user has traditionally been responsible for the choice of algorithms appropriate for a given problem. We present a system that integrates multiple description-changing and problem-solving algorithms. The purpose of the reported work is to formalize the concept of representation and to confirm the following hypothesis: An effective representation-changing system can be built from three parts: • a library of problem-solving algorithms; • a library of algorithms that improve problem descriptions; • a control module that selects algorithms for each given problem.