N. Beldiceanu and H. Simonis, A model seeker: Extracting global constraint models from positive examples, CP. pp, pp.141-157, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00754341

C. Bessiere, F. Koriche, N. Lazaar, and B. O'sullivan, Constraint acquisition, 2017.
URL : https://hal.archives-ouvertes.fr/lirmm-01276188

C. Bessiere, R. Coletta, E. Hebrard, G. Katsirelos, N. Lazaar et al., Constraint acquisition via partial queries, IJCAI. pp, pp.475-481, 2013.
URL : https://hal.archives-ouvertes.fr/lirmm-00830325

C. Bessiere, R. Coletta, and F. Koriche, A sat-based version space algorithm for acquiring constraint satisfaction problems, pp.23-34, 2005.
URL : https://hal.archives-ouvertes.fr/lirmm-00106044

, One ASP encoding that we discovered in a public repository worked mostly by pure luck. The following constraint :-queen(X1,Y1)

, works because abs is not actually absolute value but an uninterpreted function, essentially it checks X == Y , and that is indeed the found solution. (This kind of bugs would be extremely hard to find using traditional debuggers, since technically the encoding produced correct solutions.). Also, while working on the aggregate extension use-case, we discovered a subtle bug: the case of a single celebrity was not handled correctly

R. Bird and S. Curtis, Functional pearls: Finding celebrities: A lesson in functional programming, J. Funct. Program, vol.16, issue.1, pp.13-20, 2006.

G. Brewka, J. Delgrande, J. Romero, and T. Schaub, Asprin: Customizing answer set preferences without a headache, AAAI. pp, pp.1467-1474, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01187001

B. Cabon, S. De-givry, L. Lobjois, T. Schiex, and J. Warners, Radio link frequency assignment, Constraints, vol.4, issue.1, pp.79-89, 1999.

B. De-cat, B. Bogaerts, M. Bruynooghe, and M. Denecker, Predicate logic as a modelling language: The IDP system, 2014.

L. De-raedt, Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies), 2008.

T. Eiter, G. Ianni, and T. Krennwallner, Reasoning web. semantic technologies for information systems. chap. Answer Set Programming: A Primer, pp.40-110, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01913203

T. Eiter and A. Polleres, Towards automated integration of guess and check programs in answer set programming: a meta-interpreter and applications, TPLP, vol.6, issue.1-2, pp.23-60, 2006.

W. Faber, G. Pfeifer, and N. Leone, Semantics and complexity of recursive aggregates in answer set programming, Artificial Intelligence, vol.175, issue.1, pp.278-298, 2011.

M. Gebser, R. Kaminski, B. Kaufmann, and T. Schaub, Answer Set Solving in Practice, Synthesis Lectures on Artificial Intelligence and Machine Learning, 2012.

M. Gebser, J. Pührer, T. Schaub, and H. Tompits, A meta-programming technique for debugging answer-set programs

M. Gelfond and V. Lifschitz, The stable model semantics for logic programming, ICLP/SLP, vol.88, pp.1070-1080, 1988.

I. P. Gent, C. Jefferson, and P. Nightingale, Complexity of n-queens completion, J. Artif. Intell. Res, vol.59, pp.815-848, 2017.

S. Gulwani, J. Hernandez-orallo, E. Kitzelmann, S. H. Muggleton, U. Schmid et al., Inductive programming meets the real world, Commun. ACM, vol.58, issue.11, pp.90-99, 2015.

S. Hölldobler and L. Schweizer, Answer set programming and clasp a tutorial, YSIP. p, p.77, 2014.

J. Jeon, X. Qiu, J. S. Foster, and A. S. Lezama, Jsketch: sketching for java, ESEC/FSE, pp.934-937, 2015.

A. Lallouet, M. Lopez, L. Martin, and C. Vrain, On learning constraint problems, ICTAI. pp, pp.45-52, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01016891

M. Law, A. Russo, and K. Broda, Inductive Learning of Answer Set Programs, pp.311-325, 2014.

M. Law, A. Russo, and K. Broda, The ILASP system for learning answer set programs, 2015.

M. Law, A. Russo, and K. Broda, Iterative learning of answer set programs from context dependent examples, TPLP, vol.16, issue.5-6, pp.834-848, 2016.

A. S. Lezama, The sketching approach to program synthesis, APLAS. pp, pp.4-13, 2009.

A. S. Lezama, Program sketching. STTT, vol.15, pp.475-495, 2013.

T. Li, M. D. Vos, J. Padget, K. Satoh, and T. Balke, Debugging ASP using ILP, Technical Communications ICLP, 2015.

V. Lifschitz, What is answer set programming?, 2008.

S. Muggleton and L. De-raedt, Inductive logic programming: Theory and methods, Journal of Logic Programming, vol.19, issue.20, pp.629-679, 1994.

S. H. Muggleton, D. Lin, N. Pahlavi, and A. Tamaddoni-nezhad, Metainterpretive learning: application to grammatical inference, Machine Learning, vol.94, issue.1, pp.25-49, 2014.

S. H. Muggleton, D. Lin, and A. Tamaddoni-nezhad, Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited, Machine Learning, vol.100, issue.1, 2015.

J. Oetsch, J. Pührer, and H. Tompits, Stepping through an Answer-Set Program, pp.134-147, 2011.

A. Özgür, CSPLib problem 110: Peaceably co-existing armies of queens, 2015.

O. Ray, Nonmonotonic abductive inductive learning, special Issue: Abduction and Induction in Artificial Intelligence, vol.7, pp.329-340, 2009.

F. Rossi and P. Van-beek, Handbook of Constraint Programming, 2006.

R. Singh, R. Singh, Z. Xu, R. Krosnick, and A. Solar-lezama, Modular synthesis of sketches using models, Verification, Model Checking, and Abstract Interpretation, pp.395-414, 2014.