Abstract
In this chapter we present the recent results on constraint acquisition obtained by the Coconut team and their collaborators. In a first part we show how to learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments to subsets of the variables as positive or negative. We provide an algorithm, called QuAcq, that, given a negative example, finds a constraint of the target network in a number of queries logarithmic in the size of the example. In a second part, we show that using some background knowledge may improve the acquisition process a lot. We introduce the concept of generalization query based on an aggregation of variables into types. We propose a generalization algorithm together with several strategies that we incorporate in QuAcq. Finally we evaluate our algorithms on some benchmarks.
Sections 3 and 4 of this paper describe material published in [9], Sect. 5 describes material published in [8], and Sect. 6 describes results coming from both of these two papers.
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Notes
- 1.
This operation could proactively be done in QuAcq, just after line 11, but we preferred the lazy mode as this is a computationally expensive operation.
References
Angluin, D.: Queries and concept learning. Mach. Learn. 2(4), 319–342 (1987)
Beldiceanu, N., Carlsson, M., Rampon, J.: Global constraint catalog. Technical report, T2005: 08, Swedish Institute of Computer Science, Kista, Sweden, May 2005
Beldiceanu, N., Simonis, H.: A model seeker: extracting global constraint models from positive examples. In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 141–157. Springer, Heidelberg (2012)
Bessiere, C., Coletta, R., Freuder, E.C., O’Sullivan, B.: Leveraging the learning power of examples in automated constraint acquisition. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 123–137. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30201-8_12
Bessiere, C., Coletta, R., Koriche, F., O’Sullivan, 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. 23–34. Springer, Heidelberg (2005). doi:10.1007/11564096_8
Bessiere, C., Coletta, R., O’Sullivan, B., Paulin, M.: Query-driven constraint acquisition. In: Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India, pp. 44–49 (2007)
Bessiere, C., Koriche, F., Lazaar, N., O’Sullivan, B.: Constraint acquisition. Artif. Intell. (in press)
Bessiere, C., Coletta, R., Daoudi, A., Lazaar, N., Mechqrane, Y., Bouyakhf, E.: Boosting constraint acquisition via generalization queries. In: Proceedings of the 21st European Conference on Artificial Intelligence. Frontiers in Artificial Intelligence and Applications, vol. 263, pp. 99–104. IOS Press, Prague (2014)
Bessiere, C., Coletta, R., Hebrard, E., Katsirelos, G., Lazaar, N., Narodytska, N., Quimper, C., Walsh, T.: Constraint acquisition via partial queries. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, pp. 475–481. IJCAI/AAAI, Beijing (2013)
Bessiere, C., Cordier, M.: Arc-consistency and arc-consistency again. In: Proceedings of the 11th National Conference on Artificial Intelligence, pp. 108–113. AAAI Press/The MIT Press, Washington, D.C. (1993)
Cabon, B., de Givry, S., Lobjois, L., Schiex, T., Warners, J.P.: Radio link frequency assignment. Constraints 4(1), 79–89 (1999)
De Bruijn, N.: Asymptotic Methods in Analysis. Dover Books on Mathematics. Dover Publications, New York (1970)
Freuder, E.C., Wallace, R.J.: Suggestion strategies for constraint-based matchmaker agents. In: Maher, M., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 192–204. Springer, Heidelberg (1998). doi:10.1007/3-540-49481-2_15
Gent, I., Walsh, T.: CSPLib: a benchmark library for constraints. http://www.csplib.org/ (1999)
Junker, U.: QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems. In: Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI 2004), San Jose, CA, pp. 167–172 (2004)
Lallouet, A., Lopez, M., Martin, L., Vrain, C.: On learning constraint problems. In: Proceedings of the 22nd IEEE International Conference on Tools for Artificial Intelligence (IEEE-ICTAI 2010), Arras, France, pp. 45–52 (2010)
Mason, J.: Purdey’s general store. Dell Mag. 54, 10 (1997)
Paulin, M., Bessiere, C., Sallantin, J.: Automatic design of robot behaviors through constraint network acquisition. In: Proceedings of the 20th IEEE International Conference on Tools for Artificial Intelligence (IEEE-ICTAI 2008), Dayton, OH, pp. 275–282 (2008)
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Bessiere, C. et al. (2016). New Approaches to Constraint Acquisition. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., Pedreschi, D. (eds) Data Mining and Constraint Programming. Lecture Notes in Computer Science(), vol 10101. Springer, Cham. https://doi.org/10.1007/978-3-319-50137-6_3
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