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Permanent link (DOI): https://doi.org/10.7939/R3FW8W

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Additive abstraction-based heuristics Open Access

Descriptions

Other title
Subject/Keyword
heuristic search
additive
heuristics
abstraction
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Yang, Fan
Supervisor and department
Culberson, Joseph (Computing Science)
Holte, Robert (Computing Science)
Examining committee member and department
Buro, Michael (Computing Science)
Gannon, Terry (Mathematical Sciences)
Schaeffer, Jonathan (Computing Science)
Hansen, Eric (Computer Science and Engineering, Mississippi State University)
Department
Department of Computing Science
Specialization

Date accepted
2011-01-25T20:55:47Z
Graduation date
2011-06
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
In this thesis, we study theoretically and empirically the additive abstraction-based heuristics. First we present formal general definitions for abstractions that extend to general additive abstractions. We show that the general definition makes proofs of admissibility, consistency, and additivity easier, by proving that several previous methods for defining abstractions and additivity satisfy three imple conditions. Then we investigate two general methods for defining additive abstractions and run experiments to determine the effectiveness of these methods for two benchmark state spaces: TopSpin and the Pancake puzzle. Third, we propose that the accuracy of the heuristics generated by abstraction can be improved by checking for infeasibility. The theory and experiments demonstrate the approach to detect infeasibility and the application of this technique to different domains. Finally, we explore the applications of additive abstraction-based heuristics in two state spaces with nonuniform edge costs: the Sequential Ordering Problem (SOP) and the weighted Pancake puzzle. We formalize a novel way of generating additive and non-additive heuristics for these state spaces. Furthermore, we investigate the key concepts to generate good additive and non-additive abstractions. Experiments show that compared to some simple alternative heuristics, well chosen abstractions can enhance the quality of suboptimal solutions for large SOP instances and reduce search time for the weighted Pancake problems.
Language
English
DOI
doi:10.7939/R3FW8W
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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