{"type":"ScholarlyArticle","title":"Meta-Interpretive Learning Using HEX-Programs","authors":[{"name":"Tobias Kaminski","slug":"tobias-kaminski","given":"Tobias","family":"Kaminski","affiliation":"Technical University of Vienna (TU Wien) , Vienna , Austria"},{"name":"Thomas Eiter","slug":"thomas-eiter","given":"Thomas","family":"Eiter","affiliation":"Technical University of Vienna (TU Wien) , Vienna , Austria"},{"name":"Katsumi Inoue","slug":"katsumi-inoue","given":"Katsumi","family":"Inoue","affiliation":"National Institute of Informatics , Tokyo , Japan"}],"abstract":"Meta-Interpretive Learning (MIL) is a recent approach for Inductive Logic Programming (ILP) implemented in Prolog. Alternatively, MIL-problems can be solved by using Answer Set Programming (ASP), which may result in performance gains due to efficient conflict propagation. However, a straightforward MIL-encoding results in a huge size of the ground program and search space. To address these challenges, we encode MIL in the HEX-extension of ASP, which mitigates grounding issues, and we develop novel pruning techniques.","doi":"10.24963/ijcai.2019/860","url":"https://ijcai.org/proceedings/2019/860","pdf":"https://ijcai.org/proceedings/2019/0860.pdf","markdown":"https://ijcai.org/proceedings/2019/860.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/860","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Best Sister Conferences","track":"Best Sister Conferences","number":860,"pages":{"first":6186,"last":6190},"keywords":["Knowledge Representation and Reasoning: Non-monotonic Reasoning","Machine Learning: Relational Learning"],"videos":[],"references":[{"position":1,"raw":"[Cropper and Muggleton, 2016a] Andrew Cropper and Stephen H. Muggleton. Learning higher-order logic programs through abstraction and invention. In IJCAI 2016, pages 1418-1424. IJCAI/AAAI Press, 2016.","title":"Learning higher-order logic programs through abstraction and invention","authors":"Muggleton Cropper, Andrew Cropper, Stephen H Muggleton","year":"2016","venue":"IJCAI 2016","doi":null,"url":null,"internal_url":"/proceedings/2016/204"},{"position":2,"raw":"[Cropper and Muggleton, 2016b] Andrew Cropper and Stephen H. Muggleton. Metagol system. https://github.com/metagol/metagol, 2016.","title":"Metagol system","authors":"Muggleton Cropper, Andrew Cropper, Stephen H Muggleton","year":"2016","venue":null,"doi":null,"url":"https://github.com/metagol/metagol,2016","internal_url":null},{"position":3,"raw":"[Eiter et al., 2009] Thomas Eiter, Giovambattista Ianni, and Thomas Krennwallner. Answer set programming: A primer. In Reasoning Web 2009, Tutorial Lectures, vol- ume 5689 of LNCS, pages 40-110. Springer, 2009.","title":"Answer set programming: A primer","authors":"Thomas Eiter, Giovambattista Ianni, Thomas Krennwallner","year":"2009","venue":"Reasoning Web 2009","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Eiter et al., 2016] Thomas Eiter, Michael Fink, Giovambat- tista Ianni, Thomas Krennwallner, Christoph Redl, and Peter Schüller. A model building framework for an- swer set programming with external computations. TPLP, 16(4):418-464, 2016.","title":"A model building framework for answer set programming with external computations","authors":"Thomas Eiter, Michael Fink, Giovambattista Ianni, Thomas Krennwallner, Christoph Redl, Peter Schüller","year":"2016","venue":"TPLP","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Faber et al., 2011] Wolfgang Faber, Nicola Leone, and Ger- ald Pfeifer. Semantics and complexity of recursive aggregates in answer set programming. Artif. Intell., 175(1):278-298, 2011.","title":"Semantics and complexity of recursive aggregates in answer set programming","authors":"Faber","year":"2011","venue":"Artif. Intell","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Gebser et al., 2012] Martin Gebser, Benjamin Kaufmann, and Torsten Schaub. Conflict-driven answer set solving: From theory to practice. Artif. Intell., 187-188:52-89, Au- gust 2012.","title":"Conflict-driven answer set solving: From theory to practice","authors":"Gebser","year":"2012","venue":"Artif. Intell","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gelfond and Lifschitz, 1991] Michael Gelfond and Vladimir Lifschitz. Classical negation in logic pro- grams and disjunctive databases. New Generation Computing, 9(3-4):365-386, 1991.","title":"Classical negation in logic programs and disjunctive databases","authors":"Michael Gelfond, Vladimir Lifschitz","year":"1991","venue":"New Generation Computing","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kaminski et al., 2018a] Tobias Kaminski, Thomas Eiter, and Katsumi Inoue. Exploiting answer set program- ming with external sources for meta-interpretive learning. TPLP, 18(3-4):571-588, 2018.","title":"Exploiting answer set programming with external sources for meta-interpretive learning","authors":"Kaminski","year":"2018","venue":"TPLP","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kaminski et al., 2018b] Tobias Kaminski, Thomas Eiter, and Katsumi Inoue. Efficiently encoding meta-interpretive learning by answer set programming (work in progress). http://ilp2018.unife.it/wp-content/uploads/2018/08/ Efficiently-Encoding-Meta-Interpretive-Learning-by- Answer-Set-Programming.pdf, 2018.","title":"Efficiently encoding meta-interpretive learning by answer set programming","authors":"Kaminski","year":"2018","venue":null,"doi":null,"url":"http://ilp2018.unife.it/wp-content/uploads/2018/08/Efficiently-Encoding-Meta-Interpretive-Learning-by-Answer-Set-Programming.pdf","internal_url":null},{"position":10,"raw":"[Larson and Michalski, 1977] J. Larson and Ryszard S. Michalski. Inductive inference of VL decision rules. SIGART Newsletter, 63:38-44, 1977.","title":"Inductive inference of VL decision rules","authors":"J Larson, Ryszard S Michalski","year":"1977","venue":"SIGART Newsletter","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Law et al., 2014] Mark Law, Alessandra Russo, and Krysia Broda. Inductive learning of answer set programs. In JELIA 2014, volume 8761 of LNCS, pages 311-325. Springer, 2014.","title":"Inductive learning of answer set programs","authors":"Mark Law, Alessandra Russo, Krysia Broda","year":"2014","venue":"JELIA 2014","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Michie et al., 1994] Donald Michie, Stephen Muggleton, David Page, and Ashwin Srinivasan. To the interna- tional computing community: A new east-west challenge. Technical report, Oxford University Computing labora- tory, UK, 1994.","title":"To the international computing community: A new east-west challenge","authors":"Donald Michie, Stephen Muggleton, David Page, Ashwin Srinivasan","year":"1994","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Muggleton et al., 2014] Stephen H. Muggleton, Dianhuan Lin, Niels Pahlavi, and Alireza Tamaddoni-Nezhad. Meta- interpretive learning: application to grammatical infer- ence. Machine Learning, 94(1):25-49, 2014.","title":"Metainterpretive learning: application to grammatical inference","authors":"H Stephen, Dianhuan Muggleton, Niels Lin, Alireza Pahlavi, Tamaddoni-Nezhad","year":"2014","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Muggleton et al., 2015] Stephen H. Muggleton, Dianhuan Lin, and Alireza Tamaddoni-Nezhad. Meta-interpretive learning of higher-order dyadic datalog: predicate inven- tion revisited. Machine Learning, 100(1):49-73, 2015.","title":"Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited","authors":"H Stephen, Dianhuan Muggleton, Alireza Lin, Tamaddoni-Nezhad","year":"2015","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Otero, 2001] Ramón P. Otero. Induction of stable mod- els. In ILP 2001, volume 2157 of LNCS, pages 193-205. Springer, 2001.","title":"Induction of stable models","authors":"P Ramón, Otero","year":"2001","venue":"ILP 2001","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Ray, 2009] Oliver Ray. Nonmonotonic abductive inductive learning. J. Applied Logic, 7(3):329-340, 2009.","title":"Nonmonotonic abductive inductive learning","authors":"Oliver Ray","year":"2009","venue":"J. Applied Logic","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}