{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T20:12:21Z","timestamp":1790367141439,"version":"4.1.0"},"reference-count":22,"publisher":"Cambridge University Press (CUP)","issue":"3-4","license":[{"start":{"date-parts":[[2018,8,10]],"date-time":"2018-08-10T00:00:00Z","timestamp":1533859200000},"content-version":"unspecified","delay-in-days":40,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Theory and Practice of Logic Programming"],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Over the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn answer set programs. We present a sound and complete algorithm which takes the input in a slightly different manner and performs an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), which to the best of our knowledge was not previously possible. The system is publicly available at<jats:uri xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:type=\"simple\" xlink:href=\"https:\/\/goo.gl\/KdWAcV\">https:\/\/goo.gl\/KdWAcV<\/jats:uri>.<\/jats:p>","DOI":"10.1017\/s1471068418000248","type":"journal-article","created":{"date-parts":[[2018,8,10]],"date-time":"2018-08-10T05:45:00Z","timestamp":1533879900000},"page":"623-637","source":"Crossref","is-referenced-by-count":7,"title":["Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples"],"prefix":"10.1017","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0089-510X","authenticated-orcid":false,"given":"ARINDAM","family":"MITRA","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"CHITTA","family":"BARAL","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2018,8,10]]},"reference":[{"key":"S1471068418000248_ref13","unstructured":"Mitra A. and Baral C. 2016. Addressing a question answering challenge by combining statistical methods with inductive rule learning and reasoning. In AAAI, pp. 2779\u20132785."},{"key":"S1471068418000248_ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5113-y"},{"key":"S1471068418000248_ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1055686.1055687"},{"key":"S1471068418000248_ref12","unstructured":"LeCun Y. 1998. The mnist database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"S1471068418000248_ref4","first-page":"1070","article-title":"The stable model semantics for logic programming","volume":"88","author":"Gelfond","year":"1988","journal-title":"ICLP\/SLP"},{"key":"S1471068418000248_ref22","unstructured":"Weston J. , Bordes A. , Chopra S. , and Mikolov T. 2015. Towards ai-complete question answering: a set of prerequisite toy tasks. arXiv preprint arXiv:1502.05698."},{"key":"S1471068418000248_ref11","doi-asserted-by":"publisher","DOI":"10.1017\/S1471068416000351"},{"key":"S1471068418000248_ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-015-5512-1"},{"key":"S1471068418000248_ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jal.2008.10.007"},{"key":"S1471068418000248_ref2","first-page":"31","volume-title":"International Conference on Inductive Logic Programming","author":"Athakravi","year":"2013"},{"key":"S1471068418000248_ref3","unstructured":"Dai W.-Z. , Muggleton S. H. , and Zhou Z.-H. 2015. Logical vision: Meta-interpretive learning for simple geometrical concepts. In ILP (Late Breaking Papers), pp. 1\u201316."},{"key":"S1471068418000248_ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23708-4_2"},{"key":"S1471068418000248_ref10","doi-asserted-by":"publisher","DOI":"10.1017\/S1471068415000198"},{"key":"S1471068418000248_ref8","doi-asserted-by":"crossref","unstructured":"Kazmi M. , Sch\u00fcller P. and Saygin Y. 2017. Improving scalability of inductive logic programming via pruning and best-effort optimisation. Expert Systems with Applications.","DOI":"10.1016\/j.eswa.2017.06.013"},{"key":"S1471068418000248_ref16","unstructured":"Otero R. 2001. Induction of stable models. Inductive Logic Programming, 193\u2013205."},{"key":"S1471068418000248_ref21","unstructured":"Wan L. , Zeiler M. , Zhang S. , Le Cun Y. , and Fergus R. 2013. Regularization of neural networks using dropconnect. In International Conference on Machine Learning, pp. 1058\u20131066."},{"key":"S1471068418000248_ref20","doi-asserted-by":"crossref","unstructured":"Sch\u00fcller P. and Kazmi M. 2017. Best-effort inductive logic programming via fine-grained cost-based hypothesis generation. arXiv preprint arXiv:1707.02729.","DOI":"10.1007\/s10994-018-5708-2"},{"key":"S1471068418000248_ref15","doi-asserted-by":"publisher","DOI":"10.1007\/BF03037227"},{"key":"S1471068418000248_ref7","unstructured":"Katzouris N. , Artikis A. , and Paliouras G. 2017. Distributed online learning of event definitions. 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