Iterative Learning of Answer Set Programs from Context Dependent Examples
In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowle...
Saved in:
| Published in: | Theory and practice of logic programming Vol. 16; no. 5-6; pp. 834 - 848 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Cambridge, UK
Cambridge University Press
01.09.2016
|
| Subjects: | |
| ISSN: | 1471-0684, 1475-3081 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy. |
|---|---|
| AbstractList | In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy. In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy. |
| Author | LAW, MARK RUSSO, ALESSANDRA BRODA, KRYSIA |
| Author_xml | – sequence: 1 givenname: MARK surname: LAW fullname: LAW, MARK email: mark.law09@imperial.ac.uk organization: Department of Computing, Imperial College London, SW7 2AZ (e-mail: mark.law09@imperial.ac.uk, a.russo@imperial.ac.uk, k.broda@imperial.ac.uk) – sequence: 2 givenname: ALESSANDRA surname: RUSSO fullname: RUSSO, ALESSANDRA email: mark.law09@imperial.ac.uk organization: Department of Computing, Imperial College London, SW7 2AZ (e-mail: mark.law09@imperial.ac.uk, a.russo@imperial.ac.uk, k.broda@imperial.ac.uk) – sequence: 3 givenname: KRYSIA surname: BRODA fullname: BRODA, KRYSIA email: mark.law09@imperial.ac.uk organization: Department of Computing, Imperial College London, SW7 2AZ (e-mail: mark.law09@imperial.ac.uk, a.russo@imperial.ac.uk, k.broda@imperial.ac.uk) |
| BookMark | eNp9kMlKBDEURYMoOH6Au4AbN6UZKoNLaaeGBgV1XaRSL01JVdImaYe_N227EEVXCck5j_vuLtr0wQNCh5ScUELV6T2tFSVS11QSQrigG2inPImKE003P--0Wv1vo92UngihkrN6B02nGaLJ_QvgGZjoez_HweFzn14h4nvI-C6GeTRjwi6GEU-Cz_CW8QUswHfgM758M-NigLSPtpwZEhx8nXvo8eryYXJTzW6vp5PzWWVLtlxxS6yulRQtI7wF6TRrO1DOCttKppmrjXXEOCKM7rSirK2J6pgyjHZOWMr30PF67iKG5yWk3Ix9sjAMxkNYpoZqIbg4U1IW9OgH-hSW0Zd0hWJan3EiWaHUmrIxpBTBNbbPpZKyaTT90FDSrCpuflVcTPrDXMR-NPH9X4d_OWZsY9_N4VuoP60PMVeNXQ |
| CitedBy_id | crossref_primary_10_1007_s10994_021_06105_4 crossref_primary_10_1016_j_artint_2020_103438 crossref_primary_10_1016_j_artint_2018_03_005 crossref_primary_10_1017_S1471068418000248 crossref_primary_10_1007_s10994_019_05843_w crossref_primary_10_1007_s10994_022_06146_3 crossref_primary_10_1016_j_artint_2022_103794 crossref_primary_10_1007_s10994_018_5708_2 crossref_primary_10_1007_s10994_024_06636_6 crossref_primary_10_1007_s10994_021_06013_7 crossref_primary_10_1007_s10994_021_06016_4 crossref_primary_10_1007_s10994_020_05934_z crossref_primary_10_1017_S147106842100051X crossref_primary_10_1007_s10994_023_06447_1 crossref_primary_10_1016_j_procs_2020_08_046 crossref_primary_10_1017_S1471068422000011 |
| Cites_doi | 10.1145/1055686.1055687 10.1007/s10994-013-5358-3 10.1007/978-3-540-39917-9_21 10.1007/s10994-013-5353-8 10.1007/BF03037227 10.1007/BF03037089 10.1007/s10994-015-5512-1 10.1017/S1471068415000198 10.1007/978-3-662-44923-3_3 10.1007/978-3-642-31951-8_12 10.1007/s10994-009-5113-y 10.1109/SASOW.2014.18 10.1016/j.jal.2008.10.007 |
| ContentType | Journal Article |
| Copyright | Copyright © Cambridge University Press 2016 |
| Copyright_xml | – notice: Copyright © Cambridge University Press 2016 |
| DBID | AAYXX CITATION 3V. 7SC 7XB 8AL 8FD 8FE 8FG 8FK ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- L7M L~C L~D M0N P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U |
| DOI | 10.1017/S1471068416000351 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Databases ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic |
| DatabaseTitle | CrossRef Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Collection ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) |
| DatabaseTitleList | Computer Science Database Computer and Information Systems Abstracts CrossRef |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| DocumentTitleAlternate | M. Law, A. Russo and K. Broda Iterative learning of answer set programs from context-dependent examples |
| EISSN | 1475-3081 |
| EndPage | 848 |
| ExternalDocumentID | 4214925121 10_1017_S1471068416000351 |
| GroupedDBID | -E. .FH 09C 09E 0E1 0R~ 123 29Q 3V. 4.4 5VS 74X 74Y 7~V 8FE 8FG 8R4 8R5 AAAZR AABES AABWE AACJH AAEED AAFUK AAGFV AAKTX AANRG AARAB AASVR AAUKB AAYOK ABBXD ABITZ ABJNI ABKKG ABMWE ABMYL ABQTM ABQWD ABROB ABTCQ ABUWG ABZCX ACBMC ACCHT ACGFS ACIMK ACNCT ACQFJ ACREK ACUIJ ACUYZ ACWGA ACYZP ACZBM ACZUX ACZWT ADCGK ADDNB ADFEC ADGEJ ADKIL ADOCW ADOVH ADVJH AEBAK AEHGV AEMTW AENEX AENGE AEYYC AFFUJ AFKQG AFKRA AFKSM AFLOS AFLVW AFUTZ AGABE AGBYD AGJUD AGOOT AHQXX AHRGI AIGNW AIHIV AIOIP AISIE AJ7 AJCYY AJPFC AJQAS ALMA_UNASSIGNED_HOLDINGS ALVPG ALWZO AQJOH ARABE ARAPS ATUCA AUXHV AZQEC BBLKV BENPR BGHMG BGLVJ BLZWO BMAJL BPHCQ C0O CAG CBIIA CCPQU CCQAD CCTKK CFAFE CHEAL CJCSC COF CS3 DC4 DOHLZ DU5 DWQXO EBS EJD GNUQQ HCIFZ HG- HST HZ~ I.6 IH6 IOEEP IPYYG IS6 I~P J36 J38 J3A J9A JHPGK JQKCU K6V K7- KCGVB KFECR L98 LW7 M-V M0N NIKVX O9- OK1 OYBOY P2P P62 PQQKQ PROAC PYCCK Q2X RAMDC RCA ROL RR0 S6- S6U SAAAG T9M UT1 WFFJZ WQ3 WXU WXY WYP ZYDXJ AAYXX ABGDZ ABVKB ABVZP ABXHF ACAJB ACDLN AFFHD AFZFC AKMAY CITATION PHGZM PHGZT PQGLB 7SC 7XB 8AL 8FD 8FK JQ2 L7M L~C L~D PKEHL PQEST PQUKI PRINS Q9U PUEGO |
| ID | FETCH-LOGICAL-c416t-3c0c84765b203be6f82bde7fc5cb6282f4acf0af05a8d8712b407d27a21df5c13 |
| IEDL.DBID | P5Z |
| ISICitedReferencesCount | 30 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000386589800021&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-0684 |
| IngestDate | Thu Sep 04 16:47:13 EDT 2025 Fri Jul 25 10:11:15 EDT 2025 Sat Nov 29 04:58:36 EST 2025 Tue Nov 18 21:04:19 EST 2025 Wed Mar 13 05:56:09 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5-6 |
| Keywords | Iterative Learning Answer Set Programming Non-monotonic Inductive Logic Programming |
| Language | English |
| License | https://www.cambridge.org/core/terms |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c416t-3c0c84765b203be6f82bde7fc5cb6282f4acf0af05a8d8712b407d27a21df5c13 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PQID | 1828893062 |
| PQPubID | 43613 |
| PageCount | 15 |
| ParticipantIDs | proquest_miscellaneous_1855359766 proquest_journals_1828893062 crossref_citationtrail_10_1017_S1471068416000351 crossref_primary_10_1017_S1471068416000351 cambridge_journals_10_1017_S1471068416000351 |
| PublicationCentury | 2000 |
| PublicationDate | 20160900 2016-09-00 20160901 |
| PublicationDateYYYYMMDD | 2016-09-01 |
| PublicationDate_xml | – month: 09 year: 2016 text: 20160900 |
| PublicationDecade | 2010 |
| PublicationPlace | Cambridge, UK |
| PublicationPlace_xml | – name: Cambridge, UK – name: Cambridge |
| PublicationTitle | Theory and practice of logic programming |
| PublicationTitleAlternate | Theory and Practice of Logic Programming |
| PublicationYear | 2016 |
| Publisher | Cambridge University Press |
| Publisher_xml | – name: Cambridge University Press |
| References | S1471068416000351_ref12 Law (S1471068416000351_ref5) 2014 Srinivasan (S1471068416000351_ref15) 2001 S1471068416000351_ref11 Athakravi (S1471068416000351_ref1) 2014 S1471068416000351_ref14 S1471068416000351_ref13 Poxrucker (S1471068416000351_ref10) 2014 S1471068416000351_ref2 S1471068416000351_ref3 S1471068416000351_ref4 S1471068416000351_ref6 S1471068416000351_ref7 S1471068416000351_ref8 S1471068416000351_ref9 |
| References_xml | – ident: S1471068416000351_ref13 doi: 10.1145/1055686.1055687 – ident: S1471068416000351_ref9 doi: 10.1007/s10994-013-5358-3 – ident: S1471068416000351_ref12 doi: 10.1007/978-3-540-39917-9_21 – ident: S1471068416000351_ref3 doi: 10.1007/s10994-013-5353-8 – volume-title: Logics in Artificial Intelligence (JELIA 2014) year: 2014 ident: S1471068416000351_ref5 – ident: S1471068416000351_ref8 doi: 10.1007/BF03037227 – volume-title: Machine Learning at the Computing Laboratory year: 2001 ident: S1471068416000351_ref15 – ident: S1471068416000351_ref7 doi: 10.1007/BF03037089 – ident: S1471068416000351_ref4 doi: 10.1007/s10994-015-5512-1 – ident: S1471068416000351_ref6 doi: 10.1017/S1471068415000198 – start-page: 31 volume-title: Inductive Logic Programming year: 2014 ident: S1471068416000351_ref1 doi: 10.1007/978-3-662-44923-3_3 – ident: S1471068416000351_ref2 doi: 10.1007/978-3-642-31951-8_12 – ident: S1471068416000351_ref14 doi: 10.1007/s10994-009-5113-y – start-page: 44 volume-title: Proceedings of the Eighth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops year: 2014 ident: S1471068416000351_ref10 doi: 10.1109/SASOW.2014.18 – ident: S1471068416000351_ref11 doi: 10.1016/j.jal.2008.10.007 |
| SSID | ssj0016324 |
| Score | 2.3174164 |
| Snippet | In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm.... |
| SourceID | proquest crossref cambridge |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 834 |
| SubjectTerms | Algorithms Gain Iterative algorithms Learning Logic programming Preserving Programming Regular Papers Tasks |
| Title | Iterative Learning of Answer Set Programs from Context Dependent Examples |
| URI | https://www.cambridge.org/core/product/identifier/S1471068416000351/type/journal_article https://www.proquest.com/docview/1828893062 https://www.proquest.com/docview/1855359766 |
| Volume | 16 |
| WOSCitedRecordID | wos000386589800021&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1475-3081 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0016324 issn: 1471-0684 databaseCode: P5Z dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1475-3081 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0016324 issn: 1471-0684 databaseCode: K7- dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1475-3081 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0016324 issn: 1471-0684 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Nb9QwEB3RlgMXCgXElrJyJU4VFtk4_tgTastWVEWrVVVQxWUVj22EhLKlSUt_Pp7EiVoh7YWrY0dOxp7xeGbeA3hnhMusD8idLw0vpM-4CVhw60UwqEVZtPUV377o-dxcXk4X6cKtTmmVvU5sFbVbId2Rf4jnYBNta6byj1e_ObFGUXQ1UWhswBahJBB1w0J-H6IIBEXeVhdpyu4xRR_VJMhoaqS2ieqiafexFR7aqIcqurU7J9v_O-Nn8DSdONlht0SewyNf7cB2z-bA0uZ-AaenLcBy1H4sga7-YKvADqv6D3XzDVt0uVw1o5oU1uJa3TXsU6LRbdjsriSs4folfD2ZXRx_5ologWP8_oYLzDBaKSVtngnrVTC5dV4HlGhV9MlCUWLIypDJ0rjoYeU2uoEu12U-cUHiRLyCzWpV-dfAZKHsNFDiosfCTf3U5ii0QI2uCEGrEbwffvMybZd62aWa6eU_UhlB1ktiiQm0nLgzfq0bcjAMueoQO9Z13utld282g-BGsD88jtuOYill5Vc31EdKEZ0xpXbXv-INPIlnLNWlpe3BZnN949_CY7xtftbXY9g6ms0X52PYONN83K7ev9cE8M0 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3fTxQxEJ4gksiLKEg4Ra0Jvhga9tpu23swhgiEyx0XHtDwtm6nLTExe8gugv-Uf6Pt_grE5N548HW3bXa3029mdma-AdjR3CbGeaTW5ZqK1CVUexTUOO41Kp6Lur7i61TNZvr8fHS6BH-6WpiYVtlhYg3Udo7xH_lesIN10K2JZJ8uf9LYNSpGV7sWGo1YTNzvm-CylR_HB2F_3zN2dHj2-Zi2XQUoBuOjohwTDJAsU8MSbpz0mhnrlMcUjQwOiBc5-iT3SZprG9wJZoLPY5nK2dD6FIc8rPsIHguuVTxXE0X7qEWkPq-rmVTMJtKii6JGiup4MV4byiZ6d5fL4b5OvK8Saj13tPa_faFn8LS1qMl-cwSew5Ir1mGt61ZBWvDagPG4JpAO6E5aUtkLMvdkvyhv4jBXkdMmV60kseaG1LxdtxU5aNsEV-TwNo9cyuUL-PIgL7QJy8W8cFtAUiHNyMfETIfCjtzIMOSKo0IrvFdyALv9tmYtHJRZk0qnsn-kYABJt_MZtqTssTfIj0VTPvRTLhtGkkWDtztZufM0vaAM4F1_O8BKjBXlhZtfxzFpyoOzKeXLxUu8hSfHZyfTbDqeTV7BarAnZZOCtw3L1dW1ew0r-Kv6Xl69qc8KgW8PLXp_ASF4TC8 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Iterative+Learning+of+Answer+Set+Programs+from+Context+Dependent+Examples&rft.jtitle=Theory+and+practice+of+logic+programming&rft.au=LAW%2C+MARK&rft.au=RUSSO%2C+ALESSANDRA&rft.au=BRODA%2C+KRYSIA&rft.date=2016-09-01&rft.issn=1471-0684&rft.eissn=1475-3081&rft.volume=16&rft.issue=5-6&rft.spage=834&rft.epage=848&rft_id=info:doi/10.1017%2FS1471068416000351&rft.externalDBID=n%2Fa&rft.externalDocID=10_1017_S1471068416000351 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-0684&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-0684&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-0684&client=summon |