{"type":"ScholarlyArticle","title":"Learning Logic Programs by Discovering Higher-Order Abstractions","authors":[{"name":"Céline Hocquette","slug":"celine-hocquette","given":"Céline","family":"Hocquette","affiliation":"University of Oxford"},{"name":"Sebastijan Dumancic","slug":"sebastijan-dumancic","given":"Sebastijan","family":"Dumancic","affiliation":"TU Delft"},{"name":"Andrew Cropper","slug":"andrew-cropper","given":"Andrew","family":"Cropper","affiliation":"University of Oxford"}],"abstract":"We introduce the higher-order refactoring problem, where the goal is to compress a logic program by discovering higher-order abstractions, such as map, filter, and fold. We implement our approach in Stevie, which formulates the refactoring problem as a constraint optimisation problem. Our experiments on multiple domains, including program synthesis and visual reasoning, show that refactoring can improve the learning performance of an inductive logic programming system, specifically improving predictive accuracies by 27% and reducing learning times by 47%. We also show that Stevie can discover abstractions that transfer to multiple domains.","doi":"10.24963/ijcai.2024/379","url":"https://ijcai.org/proceedings/2024/379","pdf":"https://ijcai.org/proceedings/2024/0379.pdf","markdown":"https://ijcai.org/proceedings/2024/379.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/379","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":379,"pages":{"first":3421,"last":3429},"keywords":["Knowledge Representation and Reasoning: KRR: Logic programming","Machine Learning: ML: Symbolic methods"],"videos":[],"references":[{"position":1,"raw":"[Adé et al., 1994] Hilde Adé, Bart Malfait, and Luc De Raedt. RUTH: an ILP theory revision system. In Zbigniew W. Ras and Maria Zemankova, editors, Methodologies for Intelli- gent Systems, 8th International Symposium, ISMIS, Char- lotte, North Carolina, USA, volume 869 of Lecture Notes in Computer Science, pages 336-345. Springer, 1994.","title":"RUTH: an ILP theory revision system","authors":"Hilde Adé, Bart Malfait, Luc De, Raedt","year":"1994","venue":"Methodologies for Intelligent Systems, 8th International Symposium, ISMIS","doi":null,"url":null,"internal_url":null},{"position":2,"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":3,"raw":"[Bowers et al., 2023] Matthew Bowers, Theo X. Olausson, Lionel Wong, Gabriel Grand, Joshua B. Tenenbaum, Kevin Ellis, and Armando Solar-Lezama. Top-down synthesis for library learning. Proc. ACM Program. Lang., 7(POPL), jan 2023.","title":"Top-down synthesis for library learning","authors":"Matthew Bowers, Theo X Olausson, Lionel Wong, Gabriel Grand, Joshua B Tenenbaum, Kevin Ellis, Armando Solar-Lezama","year":"2023","venue":"Proc. ACM Program. Lang","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bundy and Li, 2023] Alan Bundy and Xue Li. Representa- tional change is integral to reasoning. Philos Trans A Math Phys Eng Sci. ., 2023.","title":"Representational change is integral to reasoning","authors":"Alan Bundy, Xue Li","year":"2023","venue":"Philos Trans A Math Phys Eng Sci","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cao et al., 2023] David Cao, Rose Kunkel, Chandrakana Nandi, Max Willsey, Zachary Tatlock, and Nadia Polikar- pova. Babble: learning better abstractions with e-graphs and anti-unification. Proceedings of the ACM on Program- ming Languages, 7(POPL):396-424, 2023.","title":"Babble: learning better abstractions with e-graphs and anti-unification","authors":"David Cao, Rose Kunkel, Chandrakana Nandi, Max Willsey, Zachary Tatlock, Nadia Polikarpova","year":"2023","venue":"Proceedings of the ACM on Programming Languages","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chaitin, 2006] Gregory Chaitin. The limits of reason. Scien- tific American, 294(3):74-81, 2006.","title":"The limits of reason","authors":"Gregory Chaitin","year":"2006","venue":"Scientific American","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cretu and Cropper, 2022] Bogdan Cretu and Andrew Crop- per. Constraint-driven multi-task learning. arXiv preprint arXiv:2208.11656, 2022.","title":"Constraint-driven multi-task learning","authors":"Bogdan Cretu","year":"2022","venue":"Andrew Cropper","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Cropper and Morel, 2021] Andrew Cropper and Rolf Morel. Learning programs by learning from failures. Mach. Learn., 110(4):801-856, 2021.","title":"Learning programs by learning from failures","authors":"Andrew Cropper, Rolf Morel","year":"2021","venue":"Mach. Learn","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Cropper et al., 2020] Andrew Cropper, Rolf Morel, and Stephen H. Muggleton. Learning higher-order logic pro- grams. Mach. Learn., 109(7):1289-1322, 2020.","title":"Learning higher-order logic programs","authors":"Andrew Cropper, Rolf Morel, Stephen H Muggleton","year":"2020","venue":"Mach. Learn","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Cropper et al., 2022] Andrew Cropper, Sebastijan Duman- cic, Richard Evans, and Stephen H. Muggleton. Inductive logic programming at 30. Mach. Learn., 111(1):147-172, 2022.","title":"Inductive logic programming at 30","authors":"Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H Muggleton","year":"2022","venue":"Mach. Learn","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Dai and Muggleton, 2021] Wang-Zhou Dai and Stephen H. Muggleton. Abductive knowledge induction from raw data. In Proceedings of the Thirtieth International Joint Confer- ence on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, pages 1845-1851, 2021.","title":"Abductive knowledge induction from raw data","authors":"Wang-Zhou Dai, Stephen H Muggleton","year":"2021","venue":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event","doi":null,"url":null,"internal_url":"/proceedings/2021/254"},{"position":12,"raw":"[De Raedt et al., 2015] Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, and Mathias Verbeke. Inducing probabilistic relational rules from probabilistic examples. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI, Buenos Aires, Argentina, pages 1835-1843, 2015.","title":"Inducing probabilistic relational rules from probabilistic examples","authors":"De Raedt","year":"2015","venue":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2015/261"},{"position":13,"raw":"[Dumančić et al., 2019] Sebastijan Dumančić, Tias Guns, Wannes Meert, and Hendrik Blockeel. Learning relational representations with auto-encoding logic programs. In 28th International Joint Conference on Artificial Intelligence, IJCAI, pages 6081-6087, 2019.","title":"Learning relational representations with auto-encoding logic programs","authors":"Dumančić","year":"2019","venue":"28th International Joint Conference on Artificial Intelligence, IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/842"},{"position":14,"raw":"[Dumančić et al., 2021] Sebastijan Dumančić, Tias Guns, and Andrew Cropper. Knowledge refactoring for inductive program synthesis. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI, pages 7271-7278, 2021.","title":"Sebastijan Dumančić, Tias Guns, and Andrew Cropper. Knowledge refactoring for inductive program synthesis","authors":"Dumančić","year":"2021","venue":"Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Ellis et al., 2018] Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, and Josh Tenen- baum. Learning libraries of subroutines for neurally-guided bayesian program induction. In Advances in Neural Infor- mation Processing Systems 31: Annual Conference on Neu- ral Information Processing Systems, NeurIPS, Montréal, Canada, pages 7816-7826, 2018.","title":"Learning libraries of subroutines for neurally-guided bayesian program induction","authors":"Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Josh Tenenbaum","year":"2018","venue":"Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Evans and Grefenstette, 2018] Richard Evans and Edward Grefenstette. Learning explanatory rules from noisy data. J. Artif. Intell. Res., 61:1-64, 2018.","title":"Learning explanatory rules from noisy data","authors":"Grefenstette Evans, Richard Evans, Edward Grefenstette","year":"2018","venue":"J. Artif. Intell. Res","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Evans et al., 2021] Richard Evans, José Hernández-Orallo, Johannes Welbl, Pushmeet Kohli, and Marek Sergot. Making sense of sensory input. Artificial Intelligence, 293:103438, 2021.","title":"Making sense of sensory input","authors":"Richard Evans, José Hernández-Orallo, Johannes Welbl, Pushmeet Kohli, Marek Sergot","year":"2021","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Feng and Muggleton, 1992] Cao Feng and Stephen H. Mug- gleton. Towards inductive generalization in higher order logic. In Proceedings of the Ninth International Workshop on Machine Learning (ML 1992), Aberdeen, Scotland, UK, pages 154-162.","title":"Muggleton. Towards inductive generalization in higher order logic","authors":"Cao Feng, H Stephen","year":null,"venue":"Proceedings of the Ninth International Workshop on Machine Learning (ML 1992)","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"Morgan Kaufmann, 1992.","title":null,"authors":"Morgan Kaufmann","year":"1992","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Jain et al., 2021] Arcchit Jain, Clément Gautrais, Angelika Kimmig, and Luc De Raedt. Learning CNF theories using MDL and predicate invention. In Proceedings of the Thirti- eth International Joint Conference on Artificial Intelligence, IJCAI 2021, pages 2599-2605, 2021.","title":"Learning CNF theories using MDL and predicate invention","authors":"Jain","year":"2021","venue":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021","doi":null,"url":null,"internal_url":"/proceedings/2021/358"},{"position":21,"raw":"[Kaminski et al., 2019] Tobias Kaminski, Thomas Eiter, and Katsumi Inoue. Meta-interpretive learning using hex- programs. In Sarit Kraus, editor, Proceedings of the Twenty- Eighth International Joint Conference on Artificial Intelli- gence, IJCAI, Macao, China, pages 6186-6190, 2019.","title":"Meta-interpretive learning using hexprograms","authors":"Kaminski","year":"2019","venue":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Kok and Domingos, 2007] Stanley Kok and Pedro M. Domingos. Statistical predicate invention. In Zoubin Ghahramani, editor, Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML), Corvallis, Oregon, USA, volume 227 of ACM International Confer- ence Proceeding Series, pages 433-440. ACM, 2007.","title":"Statistical predicate invention","authors":"Domingos Kok, Stanley Kok, Pedro M Domingos","year":"2007","venue":"Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML)","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Kramer, 2020] Stefan Kramer. A brief history of learning symbolic higher-level representations from data (and a cu- rious look forward). In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 4868-4876, 2020.","title":"A brief history of learning symbolic higher-level representations from data (and a curious look forward)","authors":"Stefan Kramer","year":"2020","venue":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020","doi":null,"url":null,"internal_url":"/proceedings/2020/678"},{"position":24,"raw":"[Kumaraswamy et al., 2015] Raksha Kumaraswamy, Phillip Odom, Kristian Kersting, David Leake, and Sriraam Natara- jan. Transfer learning via relational type matching. In IEEE International Conference on Data Mining, ICDM, Atlantic City, NJ, USA, pages 811-816. IEEE Computer Society, 2015.","title":"Transfer learning via relational type matching","authors":"Raksha Kumaraswamy, Phillip Odom, Kristian Kersting, David Leake, Sriraam Natarajan","year":"2015","venue":"IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Lavrac and Dzeroski, 1994] Nada Lavrac and Saso Dzeroski. Inductive logic programming -techniques and applications.","title":"Nada Lavrac and Saso Dzeroski. Inductive logic programming -techniques and applications","authors":"Dzeroski Lavrac","year":"1994","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"Ellis Horwood series in artificial intelligence. Ellis Hor- wood, 1994.","title":"Ellis Horwood series in artificial intelligence","authors":null,"year":"1994","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Lin et al., 2014] Dianhuan Lin, Eyal Dechter, Kevin Ellis, Joshua B. Tenenbaum, and Stephen H. Muggleton. Bias reformulation for one-shot function induction. In ECAI 2014 -21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic -Including Prestigious Applications of Intelligent Systems (PAIS 2014), volume 263 of Frontiers in Artificial Intelligence and Ap- plications, pages 525-530. IOS Press, 2014.","title":"Bias reformulation for one-shot function induction","authors":"Lin","year":"2014","venue":"Czech Republic -Including Prestigious Applications of Intelligent Systems (PAIS 2014)","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Lloyd, 2012] John W Lloyd. Foundations of logic program- ming. Springer Science & Business Media, 2012.","title":"Foundations of logic programming","authors":"John W, Lloyd","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Marcus, 2020] Gary Marcus. The next decade in AI: four steps towards robust artificial intelligence. CoRR, abs/2002.06177, 2020.","title":"The next decade in AI: four steps towards robust artificial intelligence","authors":"Gary Marcus","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[McCarthy, 1995] John McCarthy. Making robots conscious of their mental states. In Machine Intelligence 15, 1995.","title":"Making robots conscious of their mental states","authors":"John Mccarthy","year":"1995","venue":"Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Mitchell, 2021] Melanie Mitchell. Abstraction and analogy- making in artificial intelligence. CoRR, abs/2102.10717, 2021.","title":"Abstraction and analogymaking in artificial intelligence","authors":"Melanie Mitchell","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Muggleton and Buntine, 1988] Stephen H. Muggleton and Wray L. Buntine. Machine invention of first order pred- icates by inverting resolution. In John E. Laird, editor, Machine Learning, Proceedings of the Fifth International Conference on Machine Learning, Ann Arbor, Michigan, USA, pages 339-352.","title":"Machine invention of first order predicates by inverting resolution","authors":"Stephen H Muggleton, L Wray, Buntine","year":null,"venue":"Machine Learning, Proceedings of the Fifth International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"Morgan Kaufmann, 1988.","title":null,"authors":"Morgan Kaufmann","year":"1988","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Muggleton et al., 2012] Stephen H. Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter A. Flach, Katsumi Inoue, and Ashwin Srinivasan. ILP turns 20 -biography and future challenges. Mach. Learn., 86(1):3-23, 2012.","title":"ILP turns 20 -biography and future challenges","authors":"H Stephen, Luc Muggleton, David De Raedt, Ivan Poole, Peter A Bratko, Katsumi Flach, Ashwin Inoue, Srinivasan","year":"2012","venue":"Mach. Learn","doi":null,"url":null,"internal_url":null},{"position":35,"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. Mach. Learn., 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":"Mach. Learn","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Paes et al., 2006] Aline Paes, Filip Zelezný, Gerson Za- verucha, C. David Page Jr., and Ashwin Srinivasan. ILP through propositionalization and stochastic k-term DNF learning. In Inductive Logic Programming, 16th Interna- tional Conference, ILP Santiago de Compostela, Spain, Revised Selected Papers, volume 4455 of Lecture Notes in Computer Science, pages 379-393. Springer, 2006.","title":"ILP through propositionalization and stochastic k-term DNF learning","authors":"Aline Paes, Filip Zelezný, Gerson Zaverucha, C David Page Jr, Ashwin Srinivasan","year":"2006","venue":"Inductive Logic Programming, 16th International Conference, ILP Santiago de","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Paes et al., 2017] Aline Paes, Gerson Zaverucha, and Vítor Santos Costa. On the use of stochastic local search techniques to revise first-order logic theories from exam- ples. Mach. Learn., 106(2):197-241, 2017.","title":"On the use of stochastic local search techniques to revise first-order logic theories from examples","authors":"Aline Paes, Gerson Zaverucha, Vítor Santos, Costa","year":"2017","venue":"Mach. Learn","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Perron and Furnon, 2019] Laurent Perron and Vincent Furnon. Or-tools. Google.[Online]. Available: https://developers. google. com/optimization, 2019.","title":"Or-tools","authors":"Laurent Perron, Vincent Furnon","year":"2019","venue":null,"doi":null,"url":"https://developers.google.com/optimization","internal_url":null},{"position":39,"raw":"[Plotkin, 1971] G.D. Plotkin. Automatic Methods of Inductive Inference. PhD thesis, Edinburgh University, August 1971.","title":"Automatic Methods of Inductive Inference","authors":"G D Plotkin","year":"1971","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Purgal et al., 2022] Stanislaw J. Purgal, David M. Cerna, and Cezary Kaliszyk. Learning higher-order logic programs from failures. In Luc De Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Artifi- cial Intelligence, IJCAI, Vienna, Austria, pages 2726-2733, 2022.","title":"Learning higher-order logic programs from failures","authors":"Stanislaw J Purgal, David M Cerna, Cezary Kaliszyk","year":"2022","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2022/378"},{"position":41,"raw":"[Raedt and Bruynooghe, 1992] Luc De Raedt and Maurice Bruynooghe. Interactive concept-learning and constructive induction by analogy. Mach. Learn., 8:107-150, 1992.","title":"Interactive concept-learning and constructive induction by analogy","authors":"Bruynooghe ; Luc Raedt, Raedt De, Maurice Bruynooghe","year":"1992","venue":"Mach. Learn","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Raedt et al., 2008] Luc De Raedt, Kristian Kersting, Ange- lika Kimmig, Kate Revoredo, and Hannu Toivonen. Com- pressing probabilistic prolog programs. Mach. Learn., 70(2- 3):151-168, 2008.","title":"Compressing probabilistic prolog programs","authors":"Raedt","year":"2008","venue":"Mach. Learn","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Richards and Mooney, 1995] Bradley L. Richards and Ray- mond J. Mooney. Automated refinement of first-order horn- clause domain theories. Mach. Learn., 19(2):95-131, 1995.","title":"Automated refinement of first-order hornclause domain theories","authors":"L Bradley, Raymond J Richards, Mooney","year":"1995","venue":"Mach. Learn","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Rossi et al., 2006] Francesca Rossi, Peter van Beek, and Toby Walsh. Handbook of Constraint Programming (Foun- dations of Artificial Intelligence). Elsevier Science Inc., USA, 2006.","title":"Handbook of Constraint Programming (Foundations of Artificial Intelligence","authors":"Francesca Rossi, Peter Van Beek, Toby Walsh","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Russell, 2019] Stuart Russell. Human compatible: Artificial intelligence and the problem of control. Penguin, 2019.","title":"Human compatible: Artificial intelligence and the problem of control","authors":"Stuart Russell","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Saitta and Zucker, 2013] Lorenza Saitta and Jean-Daniel Zucker. Abstraction in artificial intelligence and complex systems. Springer, 2013.","title":"Abstraction in artificial intelligence and complex systems","authors":"Lorenza Saitta, Jean-Daniel Zucker","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Silver et al., 2023] Tom Silver, Rohan Chitnis, Nishanth Ku- mar, Willie McClinton, Tomás Lozano-Pérez, Leslie Pack Kaelbling, and Joshua B. Tenenbaum. Predicate invention for bilevel planning. In Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI , Thirty-Fifth Conference on In- novative Applications of Artificial Intelligence, IAAI, Thir- teenth Symposium on Educational Advances in Artificial Intelligence, EAAI, Washington, DC, USA, pages 12120- 12129. AAAI Press, 2023.","title":"Predicate invention for bilevel planning","authors":"Tom Silver, Rohan Chitnis, Nishanth Kumar, Willie Mcclinton, Tomás Lozano-Pérez, Leslie Pack Kaelbling, Joshua B Tenenbaum","year":"2023","venue":"Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI , Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI, Thirteenth Symposium on Educational Advances in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Simon, 1981] Herbert A Simon. The sciences of the artificial. MIT press, 1981.","title":"The sciences of the artificial","authors":"A Herbert, Simon","year":"1981","venue":null,"doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Sommer, 1995] Edgar Sommer. FENDER: an approach to theory restructuring (extended abstract). In Machine Learn- ing: ECML-95, 8th European Conference on Machine Learning, Heraclion, Crete, Greece, volume 912 of Lec- ture Notes in Computer Science, pages 356-359. Springer, 1995.","title":"FENDER: an approach to theory restructuring (extended abstract)","authors":"Edgar Sommer","year":"1995","venue":"Machine Learning: ECML-95, 8th European Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Srinivasan, 2001] Ashwin Srinivasan. The ALEPH manual. Machine Learning at the Computing Laboratory, Oxford University, 2001.","title":"The ALEPH manual","authors":"Ashwin Srinivasan","year":"2001","venue":"Machine Learning at the Computing Laboratory","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Torrey and Shavlik, 2009] Lisa Torrey and Jude Shavlik. Transfer learning. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, 1:242, 2009.","title":"Transfer learning","authors":"Torrey, Shavlik, Lisa Torrey, Jude Shavlik","year":"2009","venue":"Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Wrobel, 1996] Stefan Wrobel. First-order theory refinement. In Advances in Inductive Logic Programming, pages 14-33, 1996.","title":"First-order theory refinement","authors":"Stefan Wrobel","year":"1996","venue":"Advances in Inductive Logic Programming","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}