ERA

Download the full-sized PDF of Simultaneously searching with multiple algorithm settings: an alternative to parameter tuning for suboptimal single-agent searchDownload the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R37Q55

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Graduate Studies and Research, Faculty of

Collections

This file is in the following collections:

Theses and Dissertations

Simultaneously searching with multiple algorithm settings: an alternative to parameter tuning for suboptimal single-agent search Open Access

Descriptions

Other title
Subject/Keyword
parallel search
WIDA*
dovetailing
WA*
suboptimal single-agent search
WRBFS
parameter tuning
heuristic search
BULB
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Valenzano, Richard
Supervisor and department
Schaeffer, Jonathan (Computing Science)
Sturtevant, Nathan (Computing Science)
Examining committee member and department
Liu, Andy (Mathematical and Statistical Sciences)
Bulitko, Vadim (Computing Science)
Schaeffer, Jonathan (Computing Science)
Sturtevant, Nathan (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2009-10-02T16:34:26Z
Graduation date
2009-11
Degree
Master of Science
Degree level
Master's
Abstract
Many single-agent search algorithms have parameters that need to be tuned. Although settings found by offline tuning will exhibit strong average performance, properly selecting parameter settings for each problem can result in substantially reduced search effort. We consider the use of dovetailing as a way to deal with this issue. This procedure performs search with multiple parameter settings simultaneously. We present results testing the use of dovetailing with the weighted A*, weighted IDA*, weighted RBFS, and BULB algorithms on the sliding tile and pancake puzzle domains. Dovetailing will be shown to significantly improve weighted IDA*, often by several orders of magnitude, and generally enhance weighted RBFS. In the case of weighted A* and BULB, dovetailing will be shown to be an ineffective addition to these algorithms. A trivial parallelization of dovetailing will also be shown to decrease the search time in all considered domains.
Language
English
DOI
doi:10.7939/R37Q55
Rights
License granted by Richard Valenzano (valenzan@cs.ualberta.ca) on 2009-10-01T06:18:31Z (GMT): 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 the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein 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

File Details

Date Uploaded
Date Modified
2014-05-01T02:16:49.499+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 760491
Last modified: 2015:10:12 14:08:05-06:00
Filename: msc_thesis.pdf
Original checksum: 4dcd3ed8de279abdd06618e46302198e
Well formed: true
Valid: true
Page count: 119
Activity of users you follow
User Activity Date