Loading…

Time complexity of iterative-deepening-A [formula omitted]

We analyze the time complexity of iterative-deepening-A ∗ (IDA ∗ ). We first show how to calculate the exact number of nodes at a given depth of a regular search tree, and the asymptotic brute-force branching factor. We then use this result to analyze IDA ∗ with a consistent, admissible heuristic fu...

Full description

Saved in:
Bibliographic Details
Published in:Artificial intelligence 2001-06, Vol.129 (1), p.199-218
Main Authors: Korf, Richard E., Reid, Michael, Edelkamp, Stefan
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c482t-7e03904e98680a94e6c18d15075a315a97569a0d72312120a579c6ebc13236193
cites cdi_FETCH-LOGICAL-c482t-7e03904e98680a94e6c18d15075a315a97569a0d72312120a579c6ebc13236193
container_end_page 218
container_issue 1
container_start_page 199
container_title Artificial intelligence
container_volume 129
creator Korf, Richard E.
Reid, Michael
Edelkamp, Stefan
description We analyze the time complexity of iterative-deepening-A ∗ (IDA ∗ ). We first show how to calculate the exact number of nodes at a given depth of a regular search tree, and the asymptotic brute-force branching factor. We then use this result to analyze IDA ∗ with a consistent, admissible heuristic function. Previous analyses relied on an abstract analytic model, and characterized the heuristic function in terms of its accuracy, but do not apply to concrete problems. In contrast, our analysis allows us to accurately predict the performance of IDA ∗ on actual problems such as the sliding-tile puzzles and Rubik's Cube. The heuristic function is characterized by the distribution of heuristic values over the problem space. Contrary to conventional wisdom, our analysis shows that the asymptotic heuristic branching factor is the same as the brute-force branching factor. Thus, the effect of a heuristic function is to reduce the effective depth of search by a constant, relative to a brute-force search, rather than reducing the effective branching factor.
doi_str_mv 10.1016/S0004-3702(01)00094-7
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_57511517</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0004370201000947</els_id><sourcerecordid>57511517</sourcerecordid><originalsourceid>FETCH-LOGICAL-c482t-7e03904e98680a94e6c18d15075a315a97569a0d72312120a579c6ebc13236193</originalsourceid><addsrcrecordid>eNqFkEtLAzEUhYMoWKs_QZiV6CKam5kkEzcixRcUXFhXIiFm7khkHjVJi_33Tltx29XlwHfP4RxCToFdAgN59cIYK2iuGD9ncDEIXVC1R0ZQKk6V5rBPRv_IITmK8WuQudYwItcz32Lm-nbe4I9Pq6yvM58w2OSXSCvEOXa--6S32Vvdh3bR2KxvfUpYvR-Tg9o2EU_-7pi83t_NJo90-vzwNLmdUleUPFGFQxQrUJeyZFYXKB2UFQimhM1BWK2E1JZViufAgTMrlHYSPxzkPJeg8zE52_rOQ_-9wJhM66PDprEd9otohBIAAtROkA_1pZJsAMUWdKGPMWBt5sG3NqwMMLOe1GwmNeu9DAOzmdSsA262fzjUXXoMJjqPncPKB3TJVL3f4fALi117Lg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>27026760</pqid></control><display><type>article</type><title>Time complexity of iterative-deepening-A [formula omitted]</title><source>Library &amp; Information Science Abstracts (LISA)</source><source>ScienceDirect Freedom Collection 2022-2024</source><creator>Korf, Richard E. ; Reid, Michael ; Edelkamp, Stefan</creator><creatorcontrib>Korf, Richard E. ; Reid, Michael ; Edelkamp, Stefan</creatorcontrib><description>We analyze the time complexity of iterative-deepening-A ∗ (IDA ∗ ). We first show how to calculate the exact number of nodes at a given depth of a regular search tree, and the asymptotic brute-force branching factor. We then use this result to analyze IDA ∗ with a consistent, admissible heuristic function. Previous analyses relied on an abstract analytic model, and characterized the heuristic function in terms of its accuracy, but do not apply to concrete problems. In contrast, our analysis allows us to accurately predict the performance of IDA ∗ on actual problems such as the sliding-tile puzzles and Rubik's Cube. The heuristic function is characterized by the distribution of heuristic values over the problem space. Contrary to conventional wisdom, our analysis shows that the asymptotic heuristic branching factor is the same as the brute-force branching factor. Thus, the effect of a heuristic function is to reduce the effective depth of search by a constant, relative to a brute-force search, rather than reducing the effective branching factor.</description><identifier>ISSN: 0004-3702</identifier><identifier>EISSN: 1872-7921</identifier><identifier>DOI: 10.1016/S0004-3702(01)00094-7</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Artificial intelligence ; Branching factor ; Eight Puzzle ; Fifteen Puzzle ; Heuristic branching factor ; Heuristic methods ; Heuristic search ; Iterative-deepening-A [formula omitted] ; Problem solving ; Rubik's Cube ; Searching ; Sliding-tile puzzles ; Time complexity</subject><ispartof>Artificial intelligence, 2001-06, Vol.129 (1), p.199-218</ispartof><rights>2001 Elsevier Science B.V.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c482t-7e03904e98680a94e6c18d15075a315a97569a0d72312120a579c6ebc13236193</citedby><cites>FETCH-LOGICAL-c482t-7e03904e98680a94e6c18d15075a315a97569a0d72312120a579c6ebc13236193</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925,34136</link.rule.ids></links><search><creatorcontrib>Korf, Richard E.</creatorcontrib><creatorcontrib>Reid, Michael</creatorcontrib><creatorcontrib>Edelkamp, Stefan</creatorcontrib><title>Time complexity of iterative-deepening-A [formula omitted]</title><title>Artificial intelligence</title><description>We analyze the time complexity of iterative-deepening-A ∗ (IDA ∗ ). We first show how to calculate the exact number of nodes at a given depth of a regular search tree, and the asymptotic brute-force branching factor. We then use this result to analyze IDA ∗ with a consistent, admissible heuristic function. Previous analyses relied on an abstract analytic model, and characterized the heuristic function in terms of its accuracy, but do not apply to concrete problems. In contrast, our analysis allows us to accurately predict the performance of IDA ∗ on actual problems such as the sliding-tile puzzles and Rubik's Cube. The heuristic function is characterized by the distribution of heuristic values over the problem space. Contrary to conventional wisdom, our analysis shows that the asymptotic heuristic branching factor is the same as the brute-force branching factor. Thus, the effect of a heuristic function is to reduce the effective depth of search by a constant, relative to a brute-force search, rather than reducing the effective branching factor.</description><subject>Artificial intelligence</subject><subject>Branching factor</subject><subject>Eight Puzzle</subject><subject>Fifteen Puzzle</subject><subject>Heuristic branching factor</subject><subject>Heuristic methods</subject><subject>Heuristic search</subject><subject>Iterative-deepening-A [formula omitted]</subject><subject>Problem solving</subject><subject>Rubik's Cube</subject><subject>Searching</subject><subject>Sliding-tile puzzles</subject><subject>Time complexity</subject><issn>0004-3702</issn><issn>1872-7921</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><sourceid>F2A</sourceid><recordid>eNqFkEtLAzEUhYMoWKs_QZiV6CKam5kkEzcixRcUXFhXIiFm7khkHjVJi_33Tltx29XlwHfP4RxCToFdAgN59cIYK2iuGD9ncDEIXVC1R0ZQKk6V5rBPRv_IITmK8WuQudYwItcz32Lm-nbe4I9Pq6yvM58w2OSXSCvEOXa--6S32Vvdh3bR2KxvfUpYvR-Tg9o2EU_-7pi83t_NJo90-vzwNLmdUleUPFGFQxQrUJeyZFYXKB2UFQimhM1BWK2E1JZViufAgTMrlHYSPxzkPJeg8zE52_rOQ_-9wJhM66PDprEd9otohBIAAtROkA_1pZJsAMUWdKGPMWBt5sG3NqwMMLOe1GwmNeu9DAOzmdSsA262fzjUXXoMJjqPncPKB3TJVL3f4fALi117Lg</recordid><startdate>20010601</startdate><enddate>20010601</enddate><creator>Korf, Richard E.</creator><creator>Reid, Michael</creator><creator>Edelkamp, Stefan</creator><general>Elsevier B.V</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>E3H</scope><scope>F2A</scope></search><sort><creationdate>20010601</creationdate><title>Time complexity of iterative-deepening-A [formula omitted]</title><author>Korf, Richard E. ; Reid, Michael ; Edelkamp, Stefan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c482t-7e03904e98680a94e6c18d15075a315a97569a0d72312120a579c6ebc13236193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Artificial intelligence</topic><topic>Branching factor</topic><topic>Eight Puzzle</topic><topic>Fifteen Puzzle</topic><topic>Heuristic branching factor</topic><topic>Heuristic methods</topic><topic>Heuristic search</topic><topic>Iterative-deepening-A [formula omitted]</topic><topic>Problem solving</topic><topic>Rubik's Cube</topic><topic>Searching</topic><topic>Sliding-tile puzzles</topic><topic>Time complexity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Korf, Richard E.</creatorcontrib><creatorcontrib>Reid, Michael</creatorcontrib><creatorcontrib>Edelkamp, Stefan</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Library &amp; Information Sciences Abstracts (LISA)</collection><collection>Library &amp; Information Science Abstracts (LISA)</collection><jtitle>Artificial intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Korf, Richard E.</au><au>Reid, Michael</au><au>Edelkamp, Stefan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Time complexity of iterative-deepening-A [formula omitted]</atitle><jtitle>Artificial intelligence</jtitle><date>2001-06-01</date><risdate>2001</risdate><volume>129</volume><issue>1</issue><spage>199</spage><epage>218</epage><pages>199-218</pages><issn>0004-3702</issn><eissn>1872-7921</eissn><abstract>We analyze the time complexity of iterative-deepening-A ∗ (IDA ∗ ). We first show how to calculate the exact number of nodes at a given depth of a regular search tree, and the asymptotic brute-force branching factor. We then use this result to analyze IDA ∗ with a consistent, admissible heuristic function. Previous analyses relied on an abstract analytic model, and characterized the heuristic function in terms of its accuracy, but do not apply to concrete problems. In contrast, our analysis allows us to accurately predict the performance of IDA ∗ on actual problems such as the sliding-tile puzzles and Rubik's Cube. The heuristic function is characterized by the distribution of heuristic values over the problem space. Contrary to conventional wisdom, our analysis shows that the asymptotic heuristic branching factor is the same as the brute-force branching factor. Thus, the effect of a heuristic function is to reduce the effective depth of search by a constant, relative to a brute-force search, rather than reducing the effective branching factor.</abstract><pub>Elsevier B.V</pub><doi>10.1016/S0004-3702(01)00094-7</doi><tpages>20</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0004-3702
ispartof Artificial intelligence, 2001-06, Vol.129 (1), p.199-218
issn 0004-3702
1872-7921
language eng
recordid cdi_proquest_miscellaneous_57511517
source Library & Information Science Abstracts (LISA); ScienceDirect Freedom Collection 2022-2024
subjects Artificial intelligence
Branching factor
Eight Puzzle
Fifteen Puzzle
Heuristic branching factor
Heuristic methods
Heuristic search
Iterative-deepening-A [formula omitted]
Problem solving
Rubik's Cube
Searching
Sliding-tile puzzles
Time complexity
title Time complexity of iterative-deepening-A [formula omitted]
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T11%3A54%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Time%20complexity%20of%20iterative-deepening-A%20%5Bformula%20omitted%5D&rft.jtitle=Artificial%20intelligence&rft.au=Korf,%20Richard%20E.&rft.date=2001-06-01&rft.volume=129&rft.issue=1&rft.spage=199&rft.epage=218&rft.pages=199-218&rft.issn=0004-3702&rft.eissn=1872-7921&rft_id=info:doi/10.1016/S0004-3702(01)00094-7&rft_dat=%3Cproquest_cross%3E57511517%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c482t-7e03904e98680a94e6c18d15075a315a97569a0d72312120a579c6ebc13236193%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=27026760&rft_id=info:pmid/&rfr_iscdi=true