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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...
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Published in: | Artificial intelligence 2001-06, Vol.129 (1), p.199-218 |
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Main Authors: | , , |
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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 |
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∗
(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 & Information Sciences Abstracts (LISA)</collection><collection>Library & 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> |
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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] |
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