Changes of Problem Representation: Theory and Experiments / Edition 1

by Eugene Fink

Overview

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 importance 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.

Product Details

ISBN-13:	9783790815238
Publisher:	Physica-Verlag HD
Publication date:	12/05/2002
Series:	Studies in Fuzziness and Soft Computing , #110
Edition description:	2002
Pages:	358
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

I. Introduction.- 1. Motivation.- 2. Prodigy search.- II. Description changers.- 3. Primary effects.- 4. Abstraction.- 5. Summary and extensions.- III. Top-level control.- 6. Multiple representations.- 7. Statistical selection.- 8. Statistical extensions.- 9. Summary and extensions.- IV. Empirical results.- 10. Machining Domain.- 11. Sokoban Domain.- 12. Extended Strips Domain.- 13. Logistics Domain.- Concluding remarks.- References.