Relating constraint answer set programming languages and algorithms

Recently a logic programming language AC was proposed by Mellarkod et al. [1] to integrate answer set programming and constraint logic programming. Soon after that, a clingcon language integrating answer set programming and finite domain constraints, as well as an ezcsp language integrating answer s...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Artificial intelligence 2014-02, Vol.207, p.1-22
1. Verfasser: Lierler, Yuliya
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 22
container_issue
container_start_page 1
container_title Artificial intelligence
container_volume 207
creator Lierler, Yuliya
description Recently a logic programming language AC was proposed by Mellarkod et al. [1] to integrate answer set programming and constraint logic programming. Soon after that, a clingcon language integrating answer set programming and finite domain constraints, as well as an ezcsp language integrating answer set programming and constraint logic programming were introduced. The development of these languages and systems constitutes the appearance of a new AI subarea called constraint answer set programming. All these languages have something in common. In particular, they aim at developing new efficient inference algorithms that combine traditional answer set programming procedures and other methods in constraint programming. Yet, the exact relation between the constraint answer set programming languages and the underlying systems is not well understood. In this paper we address this issue by formally stating the precise relation between several constraint answer set programming languages – AC, clingcon, ezcsp – as well as the underlying systems.
doi_str_mv 10.1016/j.artint.2013.10.004
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1669849257</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0004370213001094</els_id><sourcerecordid>1669849257</sourcerecordid><originalsourceid>FETCH-LOGICAL-c415t-17b0dfcc105a3f6019a8e033e4e0a32ff88d1bd5fe3925f93eedb02546be917e3</originalsourceid><addsrcrecordid>eNp9kElLBDEQhYMoOI7-Aw99Ebx0m6W3XAQZ3GBAED2H6nSlzdDLmGQU_71pZvDoqajH92p5hFwymjHKyptNBi7YMWScMhGljNL8iCxYXfG0kpwdkwWNUioqyk_Jmfeb2Aop2YKsXrGH6O0SPY0-OIhjEhj9N7rEY0i2buocDMNM9DB2O-jQR6BNoO8mZ8PH4M_JiYHe48WhLsn7w_3b6ildvzw-r-7Wqc5ZEVJWNbQ1WjNagDAlZRJqpEJgjhQEN6auW9a0hUEheWGkQGwbyou8bFCyCsWSXO_nxqM-d-iDGqzX2Me7cNp5xcpS1nn0VhHN96h2k_cOjdo6O4D7UYyqOTO1UfvM1JzZrMaAou3qsAG8ht44GLX1f15eUylEziN3u-cwvvtl0SmvLY4aW-tQB9VO9v9Fv7KphQY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1669849257</pqid></control><display><type>article</type><title>Relating constraint answer set programming languages and algorithms</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>ScienceDirect Journals (5 years ago - present)</source><creator>Lierler, Yuliya</creator><creatorcontrib>Lierler, Yuliya</creatorcontrib><description>Recently a logic programming language AC was proposed by Mellarkod et al. [1] to integrate answer set programming and constraint logic programming. Soon after that, a clingcon language integrating answer set programming and finite domain constraints, as well as an ezcsp language integrating answer set programming and constraint logic programming were introduced. The development of these languages and systems constitutes the appearance of a new AI subarea called constraint answer set programming. All these languages have something in common. In particular, they aim at developing new efficient inference algorithms that combine traditional answer set programming procedures and other methods in constraint programming. Yet, the exact relation between the constraint answer set programming languages and the underlying systems is not well understood. In this paper we address this issue by formally stating the precise relation between several constraint answer set programming languages – AC, clingcon, ezcsp – as well as the underlying systems.</description><identifier>ISSN: 0004-3702</identifier><identifier>EISSN: 1872-7921</identifier><identifier>DOI: 10.1016/j.artint.2013.10.004</identifier><identifier>CODEN: AINTBB</identifier><language>eng</language><publisher>Oxford: Elsevier B.V</publisher><subject>(Constraint) answer set programming ; Algorithmics. Computability. Computer arithmetics ; Algorithms ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Constraint satisfaction processing ; Exact sciences and technology ; Expert systems ; Inference ; Learning and adaptive systems ; Logic programming ; Logical, boolean and switching functions ; Mathematical analysis ; Programming ; Programming languages ; Satisfiability modulo theories ; Speech and sound recognition and synthesis. Linguistics ; Theoretical computing</subject><ispartof>Artificial intelligence, 2014-02, Vol.207, p.1-22</ispartof><rights>2013</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-17b0dfcc105a3f6019a8e033e4e0a32ff88d1bd5fe3925f93eedb02546be917e3</citedby><cites>FETCH-LOGICAL-c415t-17b0dfcc105a3f6019a8e033e4e0a32ff88d1bd5fe3925f93eedb02546be917e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.artint.2013.10.004$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3549,27923,27924,45994</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=28093342$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Lierler, Yuliya</creatorcontrib><title>Relating constraint answer set programming languages and algorithms</title><title>Artificial intelligence</title><description>Recently a logic programming language AC was proposed by Mellarkod et al. [1] to integrate answer set programming and constraint logic programming. Soon after that, a clingcon language integrating answer set programming and finite domain constraints, as well as an ezcsp language integrating answer set programming and constraint logic programming were introduced. The development of these languages and systems constitutes the appearance of a new AI subarea called constraint answer set programming. All these languages have something in common. In particular, they aim at developing new efficient inference algorithms that combine traditional answer set programming procedures and other methods in constraint programming. Yet, the exact relation between the constraint answer set programming languages and the underlying systems is not well understood. In this paper we address this issue by formally stating the precise relation between several constraint answer set programming languages – AC, clingcon, ezcsp – as well as the underlying systems.</description><subject>(Constraint) answer set programming</subject><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Constraint satisfaction processing</subject><subject>Exact sciences and technology</subject><subject>Expert systems</subject><subject>Inference</subject><subject>Learning and adaptive systems</subject><subject>Logic programming</subject><subject>Logical, boolean and switching functions</subject><subject>Mathematical analysis</subject><subject>Programming</subject><subject>Programming languages</subject><subject>Satisfiability modulo theories</subject><subject>Speech and sound recognition and synthesis. Linguistics</subject><subject>Theoretical computing</subject><issn>0004-3702</issn><issn>1872-7921</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kElLBDEQhYMoOI7-Aw99Ebx0m6W3XAQZ3GBAED2H6nSlzdDLmGQU_71pZvDoqajH92p5hFwymjHKyptNBi7YMWScMhGljNL8iCxYXfG0kpwdkwWNUioqyk_Jmfeb2Aop2YKsXrGH6O0SPY0-OIhjEhj9N7rEY0i2buocDMNM9DB2O-jQR6BNoO8mZ8PH4M_JiYHe48WhLsn7w_3b6ildvzw-r-7Wqc5ZEVJWNbQ1WjNagDAlZRJqpEJgjhQEN6auW9a0hUEheWGkQGwbyou8bFCyCsWSXO_nxqM-d-iDGqzX2Me7cNp5xcpS1nn0VhHN96h2k_cOjdo6O4D7UYyqOTO1UfvM1JzZrMaAou3qsAG8ht44GLX1f15eUylEziN3u-cwvvtl0SmvLY4aW-tQB9VO9v9Fv7KphQY</recordid><startdate>20140201</startdate><enddate>20140201</enddate><creator>Lierler, Yuliya</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20140201</creationdate><title>Relating constraint answer set programming languages and algorithms</title><author>Lierler, Yuliya</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-17b0dfcc105a3f6019a8e033e4e0a32ff88d1bd5fe3925f93eedb02546be917e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>(Constraint) answer set programming</topic><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Constraint satisfaction processing</topic><topic>Exact sciences and technology</topic><topic>Expert systems</topic><topic>Inference</topic><topic>Learning and adaptive systems</topic><topic>Logic programming</topic><topic>Logical, boolean and switching functions</topic><topic>Mathematical analysis</topic><topic>Programming</topic><topic>Programming languages</topic><topic>Satisfiability modulo theories</topic><topic>Speech and sound recognition and synthesis. Linguistics</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lierler, Yuliya</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering 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><jtitle>Artificial intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lierler, Yuliya</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Relating constraint answer set programming languages and algorithms</atitle><jtitle>Artificial intelligence</jtitle><date>2014-02-01</date><risdate>2014</risdate><volume>207</volume><spage>1</spage><epage>22</epage><pages>1-22</pages><issn>0004-3702</issn><eissn>1872-7921</eissn><coden>AINTBB</coden><abstract>Recently a logic programming language AC was proposed by Mellarkod et al. [1] to integrate answer set programming and constraint logic programming. Soon after that, a clingcon language integrating answer set programming and finite domain constraints, as well as an ezcsp language integrating answer set programming and constraint logic programming were introduced. The development of these languages and systems constitutes the appearance of a new AI subarea called constraint answer set programming. All these languages have something in common. In particular, they aim at developing new efficient inference algorithms that combine traditional answer set programming procedures and other methods in constraint programming. Yet, the exact relation between the constraint answer set programming languages and the underlying systems is not well understood. In this paper we address this issue by formally stating the precise relation between several constraint answer set programming languages – AC, clingcon, ezcsp – as well as the underlying systems.</abstract><cop>Oxford</cop><pub>Elsevier B.V</pub><doi>10.1016/j.artint.2013.10.004</doi><tpages>22</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0004-3702
ispartof Artificial intelligence, 2014-02, Vol.207, p.1-22
issn 0004-3702
1872-7921
language eng
recordid cdi_proquest_miscellaneous_1669849257
source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; ScienceDirect Journals (5 years ago - present)
subjects (Constraint) answer set programming
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Constraint satisfaction processing
Exact sciences and technology
Expert systems
Inference
Learning and adaptive systems
Logic programming
Logical, boolean and switching functions
Mathematical analysis
Programming
Programming languages
Satisfiability modulo theories
Speech and sound recognition and synthesis. Linguistics
Theoretical computing
title Relating constraint answer set programming languages and algorithms
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T20%3A47%3A55IST&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=Relating%20constraint%20answer%20set%20programming%20languages%20and%20algorithms&rft.jtitle=Artificial%20intelligence&rft.au=Lierler,%20Yuliya&rft.date=2014-02-01&rft.volume=207&rft.spage=1&rft.epage=22&rft.pages=1-22&rft.issn=0004-3702&rft.eissn=1872-7921&rft.coden=AINTBB&rft_id=info:doi/10.1016/j.artint.2013.10.004&rft_dat=%3Cproquest_cross%3E1669849257%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1669849257&rft_id=info:pmid/&rft_els_id=S0004370213001094&rfr_iscdi=true