{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T10:25:09Z","timestamp":1756635909852,"version":"3.40.3"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030314224"},{"type":"electronic","value":"9783030314231"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-31423-1_7","type":"book-chapter","created":{"date-parts":[[2019,9,17]],"date-time":"2019-09-17T17:03:56Z","timestamp":1568739836000},"page":"232-249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Constraint Learning: An Appetizer"],"prefix":"10.1007","author":[{"given":"Stefano","family":"Teso","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,13]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Alur, R., et al.: Syntax-guided synthesis. In: 2013 Formal Methods in Computer-Aided Design, pp. 1\u20138. IEEE (2013)","DOI":"10.1109\/FMCAD.2013.6679385"},{"issue":"12","key":"7_CR2","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3208071","volume":"61","author":"R Alur","year":"2018","unstructured":"Alur, R., Singh, R., Fisman, D., Solar-Lezama, A.: Search-based program synthesis. Commun. ACM 61(12), 84\u201393 (2018)","journal-title":"Commun. ACM"},{"issue":"6","key":"7_CR3","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/0950-7051(96)81920-4","volume":"8","author":"R Andrews","year":"1995","unstructured":"Andrews, R., Diederich, J., Tickle, A.B.: Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl.-Based Syst. 8(6), 373\u2013389 (1995)","journal-title":"Knowl.-Based Syst."},{"issue":"4","key":"7_CR4","first-page":"319","volume":"2","author":"D Angluin","year":"1988","unstructured":"Angluin, D.: Queries and concept learning. Mach. Learn. 2(4), 319\u2013342 (1988)","journal-title":"Mach. Learn."},{"key":"7_CR5","first-page":"825","volume":"185","author":"CW Barrett","year":"2009","unstructured":"Barrett, C.W., Sebastiani, R., Seshia, S.A., Tinelli, C.: Satisfiability modulo theories. Handb. Satisf. 185, 825\u2013885 (2009)","journal-title":"Handb. Satisf."},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.artint.2015.03.003","volume":"244","author":"M Bartlett","year":"2017","unstructured":"Bartlett, M., Cussens, J.: Integer linear programming for the Bayesian network structure learning problem. Artif. Intell. 244, 258\u2013271 (2017)","journal-title":"Artif. Intell."},{"key":"7_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/978-3-642-33558-7_13","volume-title":"Principles and Practice of Constraint Programming","author":"N Beldiceanu","year":"2012","unstructured":"Beldiceanu, N., Simonis, H.: A model seeker: extracting global constraint models from positive examples. In: Milano, M. (ed.) CP 2012. LNCS, pp. 141\u2013157. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33558-7_13"},{"key":"7_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/11564096_8","volume-title":"Machine Learning: ECML 2005","author":"C Bessiere","year":"2005","unstructured":"Bessiere, C., Coletta, R., Koriche, F., O\u2019Sullivan, B.: A SAT-based version space algorithm for acquiring constraint satisfaction problems. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 23\u201334. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11564096_8"},{"key":"7_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/978-3-319-50137-6_3","volume-title":"Data Mining and Constraint Programming","author":"C Bessiere","year":"2016","unstructured":"Bessiere, C., et al.: New approaches to constraint acquisition. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O\u2019Sullivan, B., Pedreschi, D. (eds.) Data Mining and Constraint Programming. LNCS (LNAI), vol. 10101, pp. 51\u201376. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-50137-6_3"},{"issue":"1","key":"7_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/383721.383725","volume":"23","author":"S Bistarelli","year":"2001","unstructured":"Bistarelli, S., Montanari, U., Rossi, F.: Semiring-based constraint logic programming: syntax and semantics. ACM Trans. Program. Lang. Syst. (TOPLAS) 23(1), 1\u201329 (2001)","journal-title":"ACM Trans. Program. Lang. Syst. (TOPLAS)"},{"issue":"2","key":"7_CR11","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1137\/16M1080173","volume":"60","author":"L Bottou","year":"2018","unstructured":"Bottou, L., Curtis, F.E., Nocedal, J.: Optimization methods for large-scale machine learning. Siam Rev. 60(2), 223\u2013311 (2018)","journal-title":"Siam Rev."},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Boutilier, C., Regan, K., Viappiani, P.: Simultaneous elicitation of preference features and utility. In: Twenty-Fourth AAAI Conference on Artificial Intelligence (2010)","DOI":"10.1609\/aaai.v24i1.7754"},{"key":"7_CR13","unstructured":"Bunel, R.R., Turkaslan, I., Torr, P., Kohli, P., Mudigonda, P.K.: A unified view of piecewise linear neural network verification. In: Advances in Neural Information Processing Systems, pp. 4790\u20134799 (2018)"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Daum\u00e9 III, H., Marcu, D.: Learning as search optimization: approximate large margin methods for structured prediction. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 169\u2013176. ACM (2005)","DOI":"10.1145\/1102351.1102373"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"De Raedt, L., Passerini, A., Teso, S.: Learning constraints from examples. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12217"},{"key":"7_CR16","unstructured":"Dong, C., Chen, Y., Zeng, B.: Generalized inverse optimization through online learning. In: Advances in Neural Information Processing Systems, pp. 86\u201395 (2018)"},{"issue":"11","key":"7_CR17","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1145\/2736282","volume":"58","author":"S Gulwani","year":"2015","unstructured":"Gulwani, S., Hernandez-Orallo, J., Kitzelmann, E., Muggleton, S.H., Schmid, U., Zorn, B.: Inductive programming meets the real world. Commun. ACM 58(11), 90\u201399 (2015)","journal-title":"Commun. ACM"},{"key":"7_CR18","unstructured":"Guns, T., Dries, A., Tack, G., Nijssen, S., De Raedt, L.: Miningzinc: a modeling language for constraint-based mining. In: Twenty-Third International Joint Conference on Artificial Intelligence (2013)"},{"issue":"1","key":"7_CR19","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10\u201318 (2009)","journal-title":"ACM SIGKDD Explor. Newsl."},{"issue":"2\u20133","key":"7_CR20","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1561\/2200000037","volume":"7","author":"S Hanneke","year":"2014","unstructured":"Hanneke, S., et al.: Theory of disagreement-based active learning. Found. Trends\u00ae Mach. Learn. 7(2\u20133), 131\u2013309 (2014)","journal-title":"Found. Trends\u00ae Mach. Learn."},{"issue":"4","key":"7_CR21","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF02241270","volume":"44","author":"P Hansen","year":"1990","unstructured":"Hansen, P., Jaumard, B.: Algorithms for the maximum satisfiability problem. Computing 44(4), 279\u2013303 (1990)","journal-title":"Computing"},{"key":"7_CR22","unstructured":"He, H., Daume III, H., Eisner, J.M.: Learning to search in branch and bound algorithms. In: Advances in Neural Information Processing Systems, pp. 3293\u20133301 (2014)"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133\u2013142. ACM (2002)","DOI":"10.1145\/775047.775067"},{"issue":"11","key":"7_CR24","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1145\/1592761.1592783","volume":"52","author":"T Joachims","year":"2009","unstructured":"Joachims, T., Hofmann, T., Yue, Y., Yu, C.N.: Predicting structured objects with support vector machines. Commun. ACM 52(11), 97 (2009)","journal-title":"Commun. ACM"},{"issue":"5923","key":"7_CR25","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1126\/science.1165620","volume":"324","author":"RD King","year":"2009","unstructured":"King, R.D., et al.: The automation of science. Science 324(5923), 85\u201389 (2009)","journal-title":"Science"},{"key":"7_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10994-017-5640-x","volume":"106","author":"S Kolb","year":"2017","unstructured":"Kolb, S., Paramonov, S., Guns, T., De Raedt, L.: Learning constraints in spreadsheets and tabular data. Mach. Learn. 106, 1\u201328 (2017)","journal-title":"Mach. Learn."},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Kolb, S., Teso, S., Passerini, A., De Raedt, L.: Learning SMT (LRA) constraints using SMT solvers. In: IJCAI, pp. 2333\u20132340 (2018)","DOI":"10.24963\/ijcai.2018\/323"},{"key":"7_CR28","volume-title":"Probabilistic Graphical Models: Principles and Techniques","author":"D Koller","year":"2009","unstructured":"Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)"},{"issue":"7553","key":"7_CR29","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)","journal-title":"Nature"},{"key":"7_CR30","doi-asserted-by":"crossref","unstructured":"Lombardi, M., Milano, M.: Boosting combinatorial problem modeling with machine learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 5472\u20135478. AAAI Press (2018)","DOI":"10.24963\/ijcai.2018\/772"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Louche, U., Ralaivola, L.: From cutting planes algorithms to compression schemes and active learning. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2015)","DOI":"10.1109\/IJCNN.2015.7280694"},{"key":"7_CR32","doi-asserted-by":"crossref","unstructured":"McAllester, D.: Generalization bounds and consistency. In: Predicting Structured Data, pp. 247\u2013261 (2007)","DOI":"10.7551\/mitpress\/7443.003.0015"},{"issue":"2","key":"7_CR33","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/0004-3702(82)90040-6","volume":"18","author":"TM Mitchell","year":"1982","unstructured":"Mitchell, T.M.: Generalization as search. Artif. Intell. 18(2), 203\u2013226 (1982)","journal-title":"Artif. Intell."},{"key":"7_CR34","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/0743-1066(94)90035-3","volume":"19\/20","author":"S Muggleton","year":"1994","unstructured":"Muggleton, S., De Raedt, L.: Inductive logic programming: theory and methods. J. Logic Program. 19\/20, 629\u2013679 (1994)","journal-title":"J. Logic Program."},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"O\u2019Sullivan, B.: Automated modelling and solving in constraint programming. In: Twenty-Fourth AAAI Conference on Artificial Intelligence (2010)","DOI":"10.1609\/aaai.v24i1.7530"},{"issue":"3","key":"7_CR36","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1016\/j.ejor.2017.02.034","volume":"261","author":"TP Pawlak","year":"2017","unstructured":"Pawlak, T.P., Krawiec, K.: Automatic synthesis of constraints from examples using mixed integer linear programming. Eur. J. Oper. Res. 261(3), 1141\u20131157 (2017)","journal-title":"Eur. J. Oper. Res."},{"key":"7_CR37","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"03","key":"7_CR38","first-page":"1333","volume":"3","author":"S Perkins","year":"2003","unstructured":"Perkins, S., Lacker, K., Theiler, J.: Grafting: fast, incremental feature selection by gradient descent in function space. J. Mach. Learn. Res. 3(03), 1333\u20131356 (2003)","journal-title":"J. Mach. Learn. Res."},{"issue":"3\u20134","key":"7_CR39","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s10472-015-9475-5","volume":"77","author":"G Pigozzi","year":"2016","unstructured":"Pigozzi, G., Tsoukias, A., Viappiani, P.: Preferences in artificial intelligence. Ann. Math. Artif. Intell. 77(3\u20134), 361\u2013401 (2016)","journal-title":"Ann. Math. Artif. Intell."},{"key":"7_CR40","unstructured":"Platt, J.: Sequential minimal optimization: a fast algorithm for training support vector machines (1998)"},{"issue":"4","key":"7_CR41","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1023\/B:CONS.0000049206.43218.5f","volume":"9","author":"F Rossi","year":"2004","unstructured":"Rossi, F., Sperduti, A.: Acquiring both constraint and solution preferences in interactive constraint systems. Constraints 9(4), 311\u2013332 (2004)","journal-title":"Constraints"},{"key":"7_CR42","volume-title":"Handbook of Constraint Programming","author":"F Rossi","year":"2006","unstructured":"Rossi, F., Van Beek, P., Walsh, T.: Handbook of Constraint Programming. Elsevier, Amsterdam (2006)"},{"key":"7_CR43","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4175.001.0001","volume-title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","author":"B Scholkopf","year":"2001","unstructured":"Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)"},{"issue":"2","key":"7_CR44","first-page":"12","volume":"16","author":"R Sebastiani","year":"2015","unstructured":"Sebastiani, R., Tomasi, S.: Optimization modulo theories with linear rational costs. ACM Trans. Comput. Log. (TOCL) 16(2), 12 (2015)","journal-title":"ACM Trans. Comput. Log. (TOCL)"},{"issue":"1","key":"7_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00429ED1V01Y201207AIM018","volume":"6","author":"B Settles","year":"2012","unstructured":"Settles, B.: Active learning. Synth. Lect. Artif. Intell. Mach. Learn. 6(1), 1\u2013114 (2012)","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"issue":"1","key":"7_CR46","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10107-010-0420-4","volume":"127","author":"S Shalev-Shwartz","year":"2011","unstructured":"Shalev-Shwartz, S., Singer, Y., Srebro, N., Cotter, A.: Pegasos: primal estimated sub-gradient solver for svm. Math. Program. 127(1), 3\u201330 (2011)","journal-title":"Math. Program."},{"key":"7_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1613\/jair.4539","volume":"53","author":"P Shivaswamy","year":"2015","unstructured":"Shivaswamy, P., Joachims, T.: Coactive learning. J. Artif. Intell. Res. (JAIR) 53, 1\u201340 (2015)","journal-title":"J. Artif. Intell. Res. (JAIR)"},{"issue":"11","key":"7_CR48","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1145\/1168918.1168907","volume":"41","author":"A Solar-Lezama","year":"2006","unstructured":"Solar-Lezama, A., Tancau, L., Bodik, R., Seshia, S., Saraswat, V.: Combinatorial sketching for finite programs. ACM Sigplan Not. 41(11), 404\u2013415 (2006)","journal-title":"ACM Sigplan Not."},{"key":"7_CR49","volume-title":"Optimization for Machine Learning","author":"S Sra","year":"2012","unstructured":"Sra, S., Nowozin, S., Wright, S.J.: Optimization for Machine Learning. MIT Press, Cambridge (2012)"},{"key":"7_CR50","doi-asserted-by":"crossref","unstructured":"Teso, S., Dragone, P., Passerini, A.: Coactive critiquing: elicitation of preferences and features. In: AAAI (2017)","DOI":"10.1609\/aaai.v31i1.10929"},{"key":"7_CR51","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.artint.2015.04.002","volume":"244","author":"S Teso","year":"2017","unstructured":"Teso, S., Sebastiani, R., Passerini, A.: Structured learning modulo theories. Artif. Intell. 244, 166\u2013187 (2017)","journal-title":"Artif. Intell."},{"issue":"3","key":"7_CR52","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s101070100261","volume":"91","author":"MJ Todd","year":"2002","unstructured":"Todd, M.J.: The many facets of linear programming. Math. Program. 91(3), 417\u2013436 (2002)","journal-title":"Math. Program."},{"key":"7_CR53","doi-asserted-by":"crossref","unstructured":"Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: Proceedings of the Twenty-first International Conference on Machine Learning, p. 104. ACM (2004)","DOI":"10.1145\/1015330.1015341"},{"key":"7_CR54","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1145\/1968.1972","volume":"27","author":"L Valiant","year":"1984","unstructured":"Valiant, L.: A theory of the learnable. Commun. ACM 27, 1134\u20131142 (1984)","journal-title":"Commun. ACM"},{"issue":"5","key":"7_CR55","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1109\/72.788640","volume":"10","author":"V Vapnik","year":"1999","unstructured":"Vapnik, V.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988\u2013999 (1999)","journal-title":"IEEE Trans. Neural Netw."}],"container-title":["Lecture Notes in Computer Science","Reasoning Web. Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-31423-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T16:30:41Z","timestamp":1721752241000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-31423-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030314224","9783030314231"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-31423-1_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"13 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}