{"type":"ScholarlyArticle","title":"Turning 30: New Ideas in Inductive Logic Programming","authors":[{"name":"Andrew Cropper","slug":"andrew-cropper","given":"Andrew","family":"Cropper","affiliation":"University of Oxford"},{"name":"Sebastijan Dumančić","slug":"sebastijan-dumancic","given":"Sebastijan","family":"Dumancic","affiliation":"KU Leuven"},{"name":"Stephen H. Muggleton","slug":"stephen-h-muggleton","given":"Stephen H.","family":"Muggleton","affiliation":"Imperial College London"}],"abstract":"Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data. We survey recent work in inductive logic programming (ILP), a form of machine learning that induces logic programs from data, which has shown promise at addressing these limitations. We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to learning background knowledge, and the use of different technologies, notably answer set programming and neural networks. As ILP approaches 30, we also discuss directions for future research.","doi":"10.24963/ijcai.2020/673","url":"https://ijcai.org/proceedings/2020/673","pdf":"https://ijcai.org/proceedings/2020/0673.pdf","markdown":"https://ijcai.org/proceedings/2020/673.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/673","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Survey track","track":"Survey track","number":673,"pages":{"first":4833,"last":4839},"keywords":["Knowledge Representation and Reasoning: general","Machine Learning: general"],"videos":[],"references":[{"position":1,"raw":"[Albarghouthi et al., 2017] Aws Albarghouthi, Paraschos Koutris, Mayur Naik, and Calvin Smith. Constraint-based synthesis of Datalog programs. In CP, 2017.","title":"Constraint-based synthesis of Datalog programs","authors":"Albarghouthi","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Athakravi et al., 2013] Duangtida Athakravi, Domenico Corapi, Krysia Broda, and Alessandra Russo. Learning through hypothesis refinement using answer set programming. In ILP, 2013.","title":"Learning through hypothesis refinement using answer set programming","authors":"Athakravi","year":"2013","venue":"ILP","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bain and Srinivasan, 2018] Michael Bain and Ashwin Srinivasan. Identification of biological transition systems using meta- interpreted logic programs. Machine Learning, 107(7):1171-1206, 2018.","title":"Identification of biological transition systems using metainterpreted logic programs","authors":"Srinivasan Bain, Michael Bain, Ashwin Srinivasan","year":"2018","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bellodi and Riguzzi, 2015] Elena Bellodi and Fabrizio Riguzzi. Structure learning of probabilistic logic programs by searching the clause space. TPLP, 15(2):169-212, 2015.","title":"Elena Bellodi and Fabrizio Riguzzi. Structure learning of probabilistic logic programs by searching the clause space","authors":"Riguzzi Bellodi","year":"2015","venue":"TPLP","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bengio et al., 2019] Yoshua Bengio, Tristan Deleu, Nasim Ra- haman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bila- niuk, Anirudh Goyal, and Christopher J. Pal. A meta-transfer objective for learning to disentangle causal mechanisms. CoRR, abs/1901.10912, 2019.","title":"A meta-transfer objective for learning to disentangle causal mechanisms","authors":"Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher J Pal","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Blockeel and Raedt, 1998] Hendrik Blockeel and Luc De Raedt. Top-down induction of first-order logical decision trees. Artif. Intell., 101(1-2):285-297, 1998.","title":"Top-down induction of first-order logical decision trees","authors":"Raedt Blockeel, Hendrik Blockeel, Luc De, Raedt","year":"1998","venue":"Artif. Intell","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chollet, 2019] Franc ¸ois Chollet. On the measure of intelligence. CoRR, abs/1911.01547, 2019.","title":"Franc ¸ois Chollet. On the measure of intelligence","authors":"Chollet","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Cropper and Muggleton, 2015] Andrew Cropper and Stephen H. Muggleton. Learning efficient logical robot strategies involving composable objects. In IJCAI, 2015.","title":"Learning efficient logical robot strategies involving composable objects","authors":"Andrew Cropper, Stephen H Muggleton","year":"2015","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2015/482"},{"position":9,"raw":"[Cropper and Muggleton, 2016] Andrew Cropper and Stephen H. Muggleton. Metagol system. https://github.com/metagol/metagol, 2016.","title":"Metagol system","authors":"Andrew Cropper, Stephen H Muggleton","year":null,"venue":null,"doi":null,"url":"https://github.com/metagol/metagol,2016","internal_url":null},{"position":10,"raw":"[Cropper and Muggleton, 2019] Andrew Cropper and Stephen H. Muggleton. Learning efficient logic programs. Machine Learning, 108(7):1063-1083, 2019.","title":"Learning efficient logic programs","authors":"Andrew Cropper, Stephen H Muggleton","year":"2019","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Cropper and Tourret, 2019] Andrew Cropper and Sophie Tourret. Logical reduction of metarules. Machine Learning, Nov 2019.","title":"Logical reduction of metarules","authors":"Andrew Cropper, Sophie Tourret","year":"2019","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Cropper et al., 2015] Andrew Cropper, Alireza Tamaddoni-Nezhad, and Stephen H. Muggleton. Meta-interpretive learning of data transformation programs. In ILP, 2015.","title":"Meta-interpretive learning of data transformation programs","authors":"Andrew Cropper, Alireza Tamaddoni-Nezhad, Stephen H Muggleton","year":"2015","venue":"ILP","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Cropper et al., 2019] Andrew Cropper, Rolf Morel, and Stephen Muggleton. Learning higher-order logic programs. Machine Learning, Dec 2019.","title":"Learning higher-order logic programs","authors":"Andrew Cropper, Rolf Morel, Stephen Muggleton","year":"2019","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Cropper, 2019] Andrew Cropper. Playgol: Learning programs through play. In IJCAI, 2019.","title":"Playgol: Learning programs through play","authors":"Andrew Cropper","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/841"},{"position":15,"raw":"[Cropper, 2020] Andrew Cropper. Forgetting to learn logic pro- grams. In AAAI, 2020.","title":"Forgetting to learn logic programs","authors":"Andrew Cropper","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Dong et al., 2019] Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, and Denny Zhou. Neural logic machines. In ICLR, 2019.","title":"Neural logic machines","authors":"Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Denny Zhou","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Dumančić and Blockeel, 2017] Sebastijan Dumančić and Hendrik Blockeel. Clustering-based relational unsupervised representation learning with an explicit distributed representation. In IJCAI, 2017.","title":"Sebastijan Dumančić and Hendrik Blockeel. Clustering-based relational unsupervised representation learning with an explicit distributed representation","authors":"Blockeel Dumančić","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Dumančić et al., 2019] Sebastijan Dumančić, Tias Guns, Wannes Meert, and Hendrik Blockeel. Learning relational representations with auto-encoding logic programs. In IJCAI, 2019.","title":"Learning relational representations with auto-encoding logic programs","authors":"Dumančić","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/842"},{"position":19,"raw":"[Evans and Grefenstette, 2018] Richard Evans and Edward Grefen- stette. Learning explanatory rules from noisy data. J. Artif. Intell. Res., 61:1-64, 2018.","title":"Richard Evans and Edward Grefenstette. Learning explanatory rules from noisy data","authors":"Grefenstette Evans","year":"2018","venue":"J. Artif. Intell. Res","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Gebser et al., 2012] Martin Gebser, Benjamin Kaufmann, and Torsten Schaub. Conflict-driven answer set solving: From theory to practice. Artif. Intell., 187:52-89, 2012.","title":"Conflict-driven answer set solving: From theory to practice","authors":"Martin Gebser, Benjamin Kaufmann, Torsten Schaub","year":"2012","venue":"Artif. Intell","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Inoue et al., 2014] Katsumi Inoue, Tony Ribeiro, and Chiaki Sakama. Learning from interpretation transition. Machine Learn- ing, 94(1):51-79, 2014.","title":"Learning from interpretation transition","authors":"Katsumi Inoue, Tony Ribeiro, Chiaki Sakama","year":"2014","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Kaminski et al., 2019] Tobias Kaminski, Thomas Eiter, and Kat- sumi Inoue. Meta-interpretive learning using HEX-programs. In IJCAI, 2019.","title":"Meta-interpretive learning using HEX-programs","authors":"Kaminski","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/860"},{"position":23,"raw":"[Katzouris et al., 2015] Nikos Katzouris, Alexander Artikis, and Georgios Paliouras. Incremental learning of event definitions with inductive logic programming. Machine Learning, 100(2- 3):555-585, 2015.","title":"Incremental learning of event definitions with inductive logic programming","authors":"Nikos Katzouris, Alexander Artikis, Georgios Paliouras","year":"2015","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Katzouris et al., 2016] Nikos Katzouris, Alexander Artikis, and Georgios Paliouras. Online learning of event definitions. TPLP, 16(5-6):817-833, 2016.","title":"Online learning of event definitions","authors":"Nikos Katzouris, Alexander Artikis, Georgios Paliouras","year":"2016","venue":"TPLP","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Kazmi et al., 2017] Mishal Kazmi, Peter Schüller, and Yücel Say- gin. Improving scalability of inductive logic programming via pruning and best-effort optimisation. Expert Syst. Appl., 87:291- 303, 2017.","title":"Improving scalability of inductive logic programming via pruning and best-effort optimisation","authors":"Mishal Kazmi, Peter Schüller, Yücel Saygin","year":"2017","venue":"Expert Syst. Appl","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Lake et al., 2016] Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman. Building machines that learn and think like people. CoRR, abs/1604.00289, 2016.","title":"Building machines that learn and think like people","authors":"M Brenden, Lake, D Tomer, Joshua B Ullman, Samuel J Tenenbaum, Gershman","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Law et al., 2014] Mark Law, Alessandra Russo, and Krysia Broda. Inductive learning of answer set programs. In JELIA, 2014.","title":"Inductive learning of answer set programs","authors":"Mark Law, Alessandra Russo, Krysia Broda","year":"2014","venue":"JELIA","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Law et al., 2018a] Mark Law, Alessandra Russo, and Krysia Broda. The complexity and generality of learning answer set programs. Artif. Intell., 259:110-146, 2018.","title":"The complexity and generality of learning answer set programs","authors":"Law","year":"2018","venue":"Artif. Intell","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Law et al., 2018b] Mark Law, Alessandra Russo, and Krysia Broda. Inductive learning of answer set programs from noisy examples. CoRR, abs/1808.08441, 2018.","title":"Inductive learning of answer set programs from noisy examples","authors":"Law","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Law et al., 2019] Mark Law, Alessandra Russo, Elisa Bertino, Krysia Broda, and Jorge Lobo. Representing and learning gram- mars in answer set programming. In AAAI, 2019.","title":"Representing and learning grammars in answer set programming","authors":"Mark Law, Alessandra Russo, Elisa Bertino, Krysia Broda, Jorge Lobo","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Law et al., 2020] Mark Law, Alessandra Russo, Elisa Bertino, Krysia Broda, and Jorge Lobo. Fastlas: scalable inductive logic programming incorporating domain-specific optimisation criteria. In AAAI, 2020.","title":"Fastlas: scalable inductive logic programming incorporating domain-specific optimisation criteria","authors":"Mark Law, Alessandra Russo, Elisa Bertino, Krysia Broda, Jorge Lobo","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Lin et al., 2014] Dianhuan Lin, Eyal Dechter, Kevin Ellis, Joshua B. Tenenbaum, and Stephen Muggleton. Bias reformulation for one- shot function induction. In ECAI, 2014.","title":"Bias reformulation for oneshot function induction","authors":"Dianhuan Lin, Eyal Dechter, Kevin Ellis, Joshua B Tenenbaum, Stephen Muggleton","year":"2014","venue":"ECAI","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Marcus, 2018] Gary Marcus. Deep learning: A critical appraisal. CoRR, abs/1801.00631, 2018.","title":"Deep learning: A critical appraisal","authors":"Gary Marcus","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Michie, 1988] Donald Michie. Machine learning in the next five years. In EWSL, 1988.","title":"Machine learning in the next five years","authors":"Donald Michie","year":"1988","venue":"EWSL","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Muggleton and Buntine, 1988] Stephen Muggleton and Wray L. Buntine. Machine invention of first order predicates by inverting resolution. In ICML, pages 339-352, 1988.","title":"Machine invention of first order predicates by inverting resolution","authors":"Stephen Muggleton, L Wray, Buntine","year":"1988","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Muggleton and Raedt, 1994] Stephen Muggleton and Luc De Raedt. Inductive logic programming: Theory and methods. J. Log. Program., 19/20:629-679, 1994.","title":"Inductive logic programming: Theory and methods","authors":"Raedt Muggleton, Stephen Muggleton, Luc De, Raedt","year":"1994","venue":"J. Log. Program","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Muggleton et al., 2012] Stephen Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter A. Flach, Katsumi Inoue, and Ashwin Srinivasan. ILP turns 20 -biography and future challenges. Ma- chine Learning, 86(1):3-23, 2012.","title":"ILP turns 20 -biography and future challenges","authors":"Muggleton","year":"2012","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Muggleton et al., 2014] Stephen H. Muggleton, Dianhuan Lin, Niels Pahlavi, and Alireza Tamaddoni-Nezhad. Meta-interpretive learning: application to grammatical inference. Machine Learning, 94(1):25-49, 2014.","title":"Meta-interpretive 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":39,"raw":"[Muggleton et al., 2015] Stephen H. Muggleton, Dianhuan Lin, and Alireza Tamaddoni-Nezhad. Meta-interpretive learning of higher- order dyadic Datalog: predicate invention revisited. Machine Learning, 100(1):49-73, 2015.","title":"Meta-interpretive learning of higherorder 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":40,"raw":"[Muggleton et al., 2018a] S.H. Muggleton, U. Schmid, C. Zeller, A. Tamaddoni-Nezhad, and T. Besold. Ultra-strong machine learn- ing -comprehensibility of programs learned with ILP. Machine Learning, 107:1119-1140, 2018.","title":"Ultra-strong machine learning -comprehensibility of programs learned with ILP","authors":"Muggleton","year":"2018","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Muggleton et al., 2018b] Stephen Muggleton, Wang-Zhou Dai, Claude Sammut, Alireza Tamaddoni-Nezhad, Jing Wen, and Zhi- Hua Zhou. Meta-interpretive learning from noisy images. Machine Learning, 107(7):1097-1118, 2018.","title":"Meta-interpretive learning from noisy images","authors":"Muggleton","year":"2018","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Muggleton, 1991] Stephen Muggleton. Inductive logic program- ming. New Generation Comput., 8(4):295-318, 1991.","title":"Inductive logic programming","authors":"Stephen Muggleton","year":"1991","venue":"New Generation Comput","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Muggleton, 1995] Stephen Muggleton. Inverse entailment and pro- gol. New Generation Comput., 13(3&4):245-286, 1995.","title":"Inverse entailment and progol","authors":"Stephen Muggleton","year":"1995","venue":"New Generation Comput","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Quinlan, 1990] J. Ross Quinlan. Learning logical definitions from relations. Machine Learning, 5:239-266, 1990.","title":"Learning logical definitions from relations","authors":"J, Ross Quinlan","year":"1990","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Raedt et al., 2015] Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, and Mathias Verbeke. Inducing proba- bilistic relational rules from probabilistic examples. In IJCAI, 2015.","title":"Inducing probabilistic relational rules from probabilistic examples","authors":"Raedt","year":"2015","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2015/261"},{"position":46,"raw":"[Raedt et al., 2016] Luc De Raedt, Kristian Kersting, Sriraam Natarajan, and David Poole. Statistical Relational Artificial Intel- ligence: Logic, Probability, and Computation. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Clay- pool Publishers, 2016.","title":"Statistical Relational Artificial Intelligence: Logic, Probability, and Computation. Synthesis Lectures on Artificial Intelligence and Machine Learning","authors":"Raedt","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Raghothaman et al., 2020] Mukund Raghothaman, Jonathan Mendelson, David Zhao, Mayur Naik, and Bernhard Scholz. Provenance-guided synthesis of Datalog programs. PACMPL, 2020.","title":"Provenance-guided synthesis of Datalog programs","authors":"Mukund Raghothaman, Jonathan Mendelson, David Zhao, Mayur Naik, Bernhard Scholz","year":"2020","venue":"PACMPL","doi":null,"url":null,"internal_url":null},{"position":48,"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},{"position":49,"raw":"[Rocktäschel and Riedel, 2017] Tim Rocktäschel and Sebastian Riedel. End-to-end differentiable proving. In NIPS, 2017.","title":"Tim Rocktäschel and Sebastian Riedel. End-to-end differentiable proving","authors":"Riedel Rocktäschel","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Si et al., 2018] Xujie Si, Woosuk Lee, Richard Zhang, Aws Al- barghouthi, Paraschos Koutris, and Mayur Naik. Syntax-guided synthesis of Datalog programs. In ESEC/SIGSOFT. ACM, 2018.","title":"Syntax-guided synthesis of Datalog programs","authors":"Si","year":"2018","venue":"ESEC/SIGSOFT","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Si et al., 2019] Xujie Si, Mukund Raghothaman, Kihong Heo, and Mayur Naik. Synthesizing Datalog programs using numerical relaxation. In IJCAI, 2019.","title":"Synthesizing Datalog programs using numerical relaxation","authors":"Si","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/847"},{"position":52,"raw":"[Srinivasan, 2001] A. Srinivasan. The ALEPH manual. Machine Learning at the Computing Laboratory, Oxford University, 2001.","title":"The ALEPH manual","authors":"A Srinivasan","year":"2001","venue":"Machine Learning at the Computing Laboratory","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Wang et al., 2014] William Yang Wang, Kathryn Mazaitis, and William W. Cohen. Structure learning via parameter learning. In CIKM, 2014.","title":"Structure learning via parameter learning","authors":"William Yang, Wang, Kathryn Mazaitis, William W Cohen","year":"2014","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Yang et al., 2017] Fan Yang, Zhilin Yang, and William W Cohen. Differentiable learning of logical rules for knowledge base reason- ing. In NIPS 2017, 2017.","title":"Differentiable learning of logical rules for knowledge base reasoning","authors":"Fan Yang, Zhilin Yang, William W Cohen","year":"2017","venue":"NIPS 2017","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}