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Permanent link (DOI): https://doi.org/10.7939/R39W91

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Optimization for Heuristic Search Open Access

Descriptions

Other title
Subject/Keyword
search
graph embedding
manifold learning
pathfinding
optimization
heuristics
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Rayner, David Christopher Ferguson
Supervisor and department
Bowling, Michael (Computing Science)
Sturtevant, Nathan (Computer Science at University of Denver)
Examining committee member and department
Müller, Martin (Computing Science)
Weinberger, Kilian (Computer Science & Engineering at Washington University)
Schuurmans, Dale (Computing Science)
Holte, Robert (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2014-12-12T09:48:06Z
Graduation date
2015-06
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Heuristic search is a central problem in artificial intelligence. Among its defining properties is the use of a heuristic, a scalar function mapping pairs of states to an estimate of the actual distance between them. Accurate heuristics are generally correlated with faster query resolution and higher-quality solutions in a variety of settings, including GPS road navigation and video game pathfinding. Effective methods for defining heuristics remain at the forefront of heuristic search research. This research puts the task of constructing good heuristics under the lens of optimization: minimizing a loss between the true distances and the heuristic estimates, subject to admissibility and consistency constraints. Starting with first principles and well-motivated loss functions, we show several instances where performing this optimization is both feasible and tractable. This novel approach reveals previously unobserved connections to other computing subfields (e.g., graph embedding), gives new insights into previous approaches to heuristic construction (e.g., differential heuristics), and proves empirically competitive in a number of domains.
Language
English
DOI
doi:10.7939/R39W91
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.
Citation for previous publication
Rayner, C., Sturtevant, N., & Bowling, M. (2013). Subset Selection of Search Heuristics. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), (pp. 637–643). Beijing, China.Rayner, C., Bowling, M., & Sturtevant, N. (2011). Euclidean Heuristic Optimization. In Proceedings of the Twenty-Fifth National Conference on Artificial Intelligence (AAAI), (pp. 81–86). San Francisco, CA, USA.

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