Best-effort inductive logic programming via fine-grained cost-based hypothesis generation The inspire system at the inductive logic programming competition
We describe the Inspire system which participated in the first competition on inductive logic programming (ILP). Inspire is based on answer set programming (ASP). The distinguishing feature of Inspire is an ASP encoding for hypothesis space generation: given a set of facts representing the mode bias...
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| Vydáno v: | Machine learning Ročník 107; číslo 7; s. 1141 - 1169 |
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| Jazyk: | angličtina |
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01.07.2018
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| ISSN: | 0885-6125, 1573-0565 |
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| Abstract | We describe the Inspire system which participated in the first competition on inductive logic programming (ILP). Inspire is based on answer set programming (ASP). The distinguishing feature of Inspire is an ASP encoding for hypothesis space generation: given a set of facts representing the mode bias, and a set of cost configuration parameters, each answer set of this encoding represents a single rule that is considered for finding a hypothesis that entails the given examples. Compared with state-of-the-art methods that use the length of the rule body as a metric for rule complexity, our approach permits a much more fine-grained specification of the shape of hypothesis candidate rules. The Inspire system iteratively increases the rule cost limit and thereby increases the search space until it finds a suitable hypothesis. The system searches for a hypothesis that entails a single example at a time, utilizing an ASP encoding derived from the encoding used in XHAIL. We perform experiments with the development and test set of the ILP competition. For comparison we also adapted the ILASP system to process competition instances. Experimental results show that the cost parameters for the hypothesis search space are an important factor for finding hypotheses to competition instances within tight resource bounds. |
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| AbstractList | We describe the Inspire system which participated in the first competition on inductive logic programming (ILP). Inspire is based on answer set programming (ASP). The distinguishing feature of Inspire is an ASP encoding for hypothesis space generation: given a set of facts representing the mode bias, and a set of cost configuration parameters, each answer set of this encoding represents a single rule that is considered for finding a hypothesis that entails the given examples. Compared with state-of-the-art methods that use the length of the rule body as a metric for rule complexity, our approach permits a much more fine-grained specification of the shape of hypothesis candidate rules. The Inspire system iteratively increases the rule cost limit and thereby increases the search space until it finds a suitable hypothesis. The system searches for a hypothesis that entails a single example at a time, utilizing an ASP encoding derived from the encoding used in XHAIL. We perform experiments with the development and test set of the ILP competition. For comparison we also adapted the ILASP system to process competition instances. Experimental results show that the cost parameters for the hypothesis search space are an important factor for finding hypotheses to competition instances within tight resource bounds. |
| Author | Schüller, Peter Benz, Mishal |
| Author_xml | – sequence: 1 givenname: Peter orcidid: 0000-0002-1837-126X surname: Schüller fullname: Schüller, Peter email: ps@kr.tuwien.ac.at organization: Institut für Logic and Computation, Technische Universität Wien, Faculty of Engineering, Marmara University – sequence: 2 givenname: Mishal surname: Benz fullname: Benz, Mishal organization: Karlsruhe Institute of Technology, Faculty of Engineering and Natural Science, Sabanci University |
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| Cites_doi | 10.1007/BF03037227 10.1007/s10994-015-5512-1 10.1023/A:1007676901476 10.1007/978-3-642-31951-8_12 10.1016/0743-1066(94)90035-3 10.1007/s10994-013-5358-3 10.1016/B978-0-934613-40-8.50006-3 10.1145/2043174.2043195 10.1145/2736282 10.1007/978-3-319-11558-0_22 10.1007/978-3-642-20895-9_39 10.1007/s10994-011-5259-2 10.1007/s10994-014-5471-y 10.1016/j.artint.2012.04.001 10.1017/S1471068416000351 10.1017/S1471068411000305 10.1017/CBO9781139342124 10.1016/j.jal.2008.10.007 10.1017/S1471068415000198 10.1007/s10994-009-5113-y 10.1016/j.artint.2013.01.002 10.1007/978-3-642-55481-0 10.1038/nature14539 10.1007/3-540-44797-0_16 10.1016/j.artint.2010.04.002 10.2200/S00457ED1V01Y201211AIM019 10.1093/logcom/2.6.719 10.1016/j.eswa.2017.06.013 10.1609/aaai.v30i1.10354 10.1007/3-540-56602-3_129 10.1007/978-3-319-23708-4_2 10.1007/s10994-008-5079-1 |
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| Keywords | Best-effort Hypothesis generation Rule complexity Inductive logic programming Answer set programming |
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KakasACKowalskiRAToniFAbductive logic programmingJournal of Logic and Computation199226719770121897410.1093/logcom/2.6.7190778.68081 M Kazmi (5708_CR27) 2017; 87 5708_CR20 5708_CR43 TG Dietterich (5708_CR16) 2008; 73 WF Clocksin (5708_CR11) 2003 5708_CR23 M Craven (5708_CR15) 2001; 43 5708_CR28 N Katzouris (5708_CR26) 2015; 100 M Gebser (5708_CR21) 2012; 187–188 C Ansótegui (5708_CR3) 2013; 196 D Athakravi (5708_CR5) 2015 S Muggleton (5708_CR37) 1995; 13 M Gelfond (5708_CR22) 2014 SH Muggleton (5708_CR41) 2014; 94 S Muggleton (5708_CR40) 2012; 86 D Corapi (5708_CR14) 2012 AC Kakas (5708_CR25) 1992; 2 S Muggleton (5708_CR39) 1994; 19 5708_CR31 O Ray (5708_CR44) 2009; 7 5708_CR10 5708_CR32 5708_CR34 5708_CR13 5708_CR35 5708_CR36 5708_CR38 5708_CR18 C Sakama (5708_CR45) 2009; 76 5708_CR6 W Faber (5708_CR17) 2011; 175 5708_CR19 5708_CR4 5708_CR2 5708_CR1 S Gulwani (5708_CR24) 2015; 58 C Baral (5708_CR7) 2004 M Law (5708_CR29) 2015; 15 M Law (5708_CR30) 2016; 16 G Brewka (5708_CR9) 2011; 54 SH Muggleton (5708_CR42) 2015; 100 Y LeCun (5708_CR33) 2015; 521 DM Blei (5708_CR8) 2003; 3 D Corapi (5708_CR12) 2011; 11 |
| References_xml | – reference: MuggletonSHLinDPahlaviNTamaddoni-NezhadAMeta-interpretive learning: Application to grammatical inferenceMachine Learning20149412549314440610.1007/s10994-013-5358-31319.68121 – reference: Gebser, M., Kaminski, R., Kaufmann, B., & Schaub, T. (2012a). Answer set solving in practice. Morgan Claypool. – reference: LawMRussoABrodaKLearning weak constraints in answer set programmingTheory and Practice of Logic Programming2015154–5511525340683510.1017/S14710684150001981379.68073 – reference: Muggleton, S., & Buntine, W. (1992). Machine invention of first-order predicates by inverting resolution. In Proceedings of the fifth international conference on machine learning (pp. 339–352). – reference: GulwaniSHernández-OralloJKitzelmannEMuggletonSHSchmidUZornBInductive programming meets the real worldCommunications of the ACM20155811909910.1145/2736282 – reference: BaralCKnowledge representation, reasoning, and declarative problem solving2004CambridgeCambridge University Press1192.68666 – reference: BrewkaGEiterTTruszczynskiMAnswer set programming at a glanceCommunications of the ACM201154129210310.1145/2043174.2043195 – reference: LawMRussoABrodaKIterative learning of answer set programs from context dependent examplesTheory and Practice of Logic Programming2016165–6834848356935210.1017/S14710684160003511379.68074 – reference: Law, M., Russo, A., & Broda, K. (2014). Inductive learning of answer set programs. In European conference on logics in artificial intelligence (JELIA) (pp. 311–325). – reference: MuggletonSHLinDTamaddoni-NezhadAMeta-interpretive learning of higher-order dyadic datalog: Predicate invention revisitedMachine Learning201510014973337214710.1007/s10994-014-5471-y1346.68119 – reference: FaberWPfeiferGLeoneNSemantics and complexity of recursive aggregates in answer set programmingArtificial Intelligence20111751278298275235410.1016/j.artint.2010.04.0021216.68263 – reference: SakamaCInoueKBrave induction: A logical framework for learning from incomplete informationMachine Learning20097633510.1007/s10994-009-5113-y – reference: MuggletonSDe RaedtLPooleDBratkoIFlachPInoueKSrinivasanAILP turns 20: Biography and future challengesMachine Learning2012861323289066210.1007/s10994-011-5259-21243.68014 – reference: Corapi, D., Russo, A., & Lupu, E. (2010). Inductive logic programming as abductive search. In: International conference on logic programming (ICLP), technical communications, (pp. 54–63). – reference: Lifschitz, V. (2008). What is answer set programming? In AAAI conference on artificial intelligence (pp. 1594–1597). – reference: GelfondMKahlYKnowledge representation, reasoning, and the design of intelligent agents: The answer-set programming approach2014CambridgeCambridge University Press10.1017/CBO9781139342124 – reference: Law, M., Russo, A., Cussens, J., & Broda, K. (2016b). The 2016 competition on inductive logic programming. Retrieved March 29, 2017, http://ilp16.doc.ic.ac.uk/competition. – reference: Apt, K. R., Blair, H. A., & Walker, A. (1988). Towards a theory of declarative knowledge. In J. Minker (Ed.), Foundations of deductive databases and logic programming (pp. 89–148). 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| Snippet | We describe the Inspire system which participated in the first competition on inductive logic programming (ILP). Inspire is based on answer set programming... |
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| SubjectTerms | Artificial Intelligence Computer Science Control Mechatronics Natural Language Processing (NLP) Robotics Simulation and Modeling Special Issue of the Inductive Logic Programming (ILP) 2016 |
| Subtitle | The inspire system at the inductive logic programming competition |
| Title | Best-effort inductive logic programming via fine-grained cost-based hypothesis generation |
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