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[8] Cropper A, Muggleton SH. Logical minimisation of meta-rules within meta-interpretive learning. In: Proc. of the 24th Int’l Conf. on
Inductive Logic Programming. Nancy: Springer, 2015. 62–75. [doi: 10.1007/978-3-319-23708-4_5]
[9] Cropper A, Tourret S. Derivation reduction of metarules in meta-interpretive learning. In: Proc. of 28th Int’l Conf. on Inductive Logic
Programming. Ferrara: Springer, 2018. 1–21. [doi: 10.1007/978-3-319-99960-9_1]
[10] Morel R, Cropper A, Ong CHL. Typed meta-interpretive learning of logic programs. In: Proc. of the 16th European Conf. on Logics in
Artificial Intelligence. Rende: Springer, 2019. 198–213. [doi: 10.1007/978-3-030-19570-0_13]
[11] Hocquette C. Efficient instance and hypothesis space revision in meta-interpretive learning [Ph.D. Thesis]. London: Imperial College
London, 2022. [doi: 10.25560/97356]
[12] Cyrus D, Milani GA, Tamaddoni-Nezhad A. Explainable game strategy rule learning from video. In: Proc. of the 17th Int’l Rule
Challenge and 7th Doctoral Consortium @ RuleML+RR 2023 co-located with 19th Reasoning Web Summer School (RW 2023) and 15th
DecisionCAMP 2023 as part of Declarative AI 2023. 2023.
[13] Cyrus D, Trewern J, Tamaddoni-Nezhad A. Meta-interpretive learning from fractal images. In: Proc. of the 32nd Int’l Conf. on Inductive
Logic Programming. Bari: Springer, 2023. 166–174. [doi: 10.1007/978-3-031-49299-0_12]
[14] Muggleton SH, Santos JCA, Tamaddoni-Nezhad A. TopLog: ILP using a logic program declarative bias. In: Proc. of the 24th Int’l Conf.
on Logic Programming. Springer, 2008. 687–692. [doi: 10.1007/978-3-540-89982-2_58]
[15] Muggleton SH, Lin DH, Tamaddoni-Nezhad A. Meta-interpretive learning of higher-order dyadic Datalog: Predicate invention revisited.
Machine Learning, 2015, 100(1): 49–73. [doi: 10.1007/s10994-014-5471-y]
[16] Tourret S, Cropper A. SLD-resolution reduction of second-order Horn fragments. In: Proc. of the 16th European Conf. on Logics in
Artificial Intelligence. Rende: Springer, 259–276. [doi: 10.1007/978-3-030-19570-0_17]
[17] Gallier JH. Logic for Computer Science: Foundations of Automatic Theorem Proving. 2nd ed., New York: Dover Publications Inc., 2015.
[18] Nienhuys-Cheng SH, Wolf R. Foundations of Inductive Logic Programming. Berlin: Springer, 1997. [doi: 10.1007/3-540-62927-0]
[19] Cropper A, Muggleton SH. Learning efficient logic programs. Machine Learning, 2019, 108(7): 1063–1083. [doi: 10.1007/s10994-018-
5712-6]
[20] Cropper A, Morel R, Muggleton SH. Learning higher-order programs through predicate invention. In: Proc. of the 34th AAAI Conf. on
Artificial Intelligence. New York: AAAI Press, 2020. 13655–13658. [doi: 10.1609/aaai.v34i09.7113]
[21] Cropper A, Muggleton SH. Learning higher-order logic programs through abstraction and invention. In: Proc. of the 25th Int’l Joint Conf.
on Artificial Intelligence. New York: AAAI Press, 2016.
[22] Muggleton S, Buntine W. Machine invention of first-order predicates by inverting resolution. In: Proc. of the 5th Int’l Conf. on Machine
Learning. Ann Arbor: Morgan Kaufmann, 1988. 339–352. [doi: 10.1016/b978-0-934613-64-4.50040-2]
[23] Muggleton S, Feng C. Efficient induction of logic programs. In: Proc. of the 1990 Int’l Conf. on Algorithmic Learning Theory. Tokyo:
Springer, 1990. 368–381.
[24] Muggleton S. Duce, an Oracle-based approach to constructive induction. In: Proc. of the 10th Int’l Joint Conf. on Artificial Intelligence.
Milan: Morgan Kaufmann Publishers Inc., 1987. 287–292.
[25] Rouveirol C, Puget JF. Beyond inversion of resolution. In: Proc. of the 7th Int’l Conf. Austin: Morgan Kaufmann, 1990. 122–130. [doi:
10.1016/b978-1-55860-141-3.50018-3]
[26] Lavrač N, Džeroski S, Grobelnik M. Learning nonrecursive definitions of relations with Linus. In: Proc. of the 1991 European Working
Session on Learning on Machine Learning. Porto: Springer, 1991. 265–281. [doi: 10.1007/bfb0017020]
[27] De Raedt L, Bruynooghe M. CLINT: A multi-strategy interactive concept-learner and theory revision system. In: Proc. of the 1st Int’l
Workshop on Multi-strategy Learning Workshop. Virginia, 1991. 175–191.
[28] Cropper A, Tamaddoni-Nezhad A, Muggleton SH. Meta-interpretive learning of data transformation programs. In: Proc. of the 25th Int’l
Conf. on Inductive Logic Programming. Kyoto: Springer, 2016. 46–59. [doi: 10.1007/978-3-319-40566-7_4]
[29] Cropper A, Hocquette C. Learning logic programs by discovering where not to search. In: Proc. of the 37th AAAI Conf. on Artificial
Intelligence. Washington: AAAI Press, 2023. 6289–6296. [doi: 10.1609/aaai.v37i5.25774]
[30] Cropper A, Muggleton SH. Learning efficient logical robot strategies involving composable objects. In: Proc. of the 24th Int’l Conf. on
Artificial Intelligence. Buenos Aires: AAAI Press, 2015. 3423–3429.
[31] Kazmi M, Schüller P, Saygın Y. Improving scalability of inductive logic programming via pruning and best-effort optimisation. Expert
Systems with Applications, 2017, 87: 291–303. [doi: 10.1016/j.eswa.2017.06.013]
[32] Kaminski T, Eiter T, Inoue K. Meta-interpretive learning using HEX-programs. In: Proc. of the 28th Int’l Joint Conf. on Artificial

