Changes of Problem Representation [electronic resource] : Theory and Experiments /
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.
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Format: | Texto biblioteca |
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Heidelberg : Physica-Verlag HD : Imprint: Physica,
2002
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Subjects: | Computer science., Artificial intelligence., Cognitive psychology., Computer Science., Artificial Intelligence (incl. Robotics)., Cognitive Psychology., |
Online Access: | http://dx.doi.org/10.1007/978-3-7908-1774-4 |
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KOHA-OAI-TEST:2257802018-07-31T00:06:13ZChanges of Problem Representation [electronic resource] : Theory and Experiments / Fink, Eugene. author. SpringerLink (Online service) textHeidelberg : Physica-Verlag HD : Imprint: Physica,2002.engThe 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.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.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.Computer science.Artificial intelligence.Cognitive psychology.Computer Science.Artificial Intelligence (incl. Robotics).Cognitive Psychology.Springer eBookshttp://dx.doi.org/10.1007/978-3-7908-1774-4URN:ISBN:9783790817744 |
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Computer science. Artificial intelligence. Cognitive psychology. Computer Science. Artificial Intelligence (incl. Robotics). Cognitive Psychology. Computer science. Artificial intelligence. Cognitive psychology. Computer Science. Artificial Intelligence (incl. Robotics). Cognitive Psychology. |
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Computer science. Artificial intelligence. Cognitive psychology. Computer Science. Artificial Intelligence (incl. Robotics). Cognitive Psychology. Computer science. Artificial intelligence. Cognitive psychology. Computer Science. Artificial Intelligence (incl. Robotics). Cognitive Psychology. Fink, Eugene. author. SpringerLink (Online service) Changes of Problem Representation [electronic resource] : Theory and Experiments / |
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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. |
format |
Texto |
topic_facet |
Computer science. Artificial intelligence. Cognitive psychology. Computer Science. Artificial Intelligence (incl. Robotics). Cognitive Psychology. |
author |
Fink, Eugene. author. SpringerLink (Online service) |
author_facet |
Fink, Eugene. author. SpringerLink (Online service) |
author_sort |
Fink, Eugene. author. |
title |
Changes of Problem Representation [electronic resource] : Theory and Experiments / |
title_short |
Changes of Problem Representation [electronic resource] : Theory and Experiments / |
title_full |
Changes of Problem Representation [electronic resource] : Theory and Experiments / |
title_fullStr |
Changes of Problem Representation [electronic resource] : Theory and Experiments / |
title_full_unstemmed |
Changes of Problem Representation [electronic resource] : Theory and Experiments / |
title_sort |
changes of problem representation [electronic resource] : theory and experiments / |
publisher |
Heidelberg : Physica-Verlag HD : Imprint: Physica, |
publishDate |
2002 |
url |
http://dx.doi.org/10.1007/978-3-7908-1774-4 |
work_keys_str_mv |
AT finkeugeneauthor changesofproblemrepresentationelectronicresourcetheoryandexperiments AT springerlinkonlineservice changesofproblemrepresentationelectronicresourcetheoryandexperiments |
_version_ |
1756270894414561280 |