Abstract
Although propagation techniques are very important to solve constraint solving problems, heuristics are still necessary to handle non trivial problems efficiently. General principles may be defined for such heuristics (e.g. first-fail and best-promise), but problems arise in their implementation except for some limited sources of information (e.g. cardinality of variables domain). Other possibly relevant features are ignored due to the difficulty in understanding their interaction and a convenient way of integrating them. In this paper we illustrate such difficulties in a specific problem, determination of protein structure from Nuclear Magnetic Resonance (NMR) data. We show that machine learning techniques can be used to define better heuristics than the use of heuristics based on single features, or even than their combination in simple form (e.g majority vote). The technique is quite general and, with the necessary adaptations, may be applied to many other constraint satisfaction problems.
This work was supported by Fundação para a Ciência e Tecnologia, under project PROTEINA, POSI/33794/SRI/2000
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Beldiceanu, N., Contejean, E.: Introducing global constraints in CHIP. Mathl. Comput. Modelling 20, 97–123 (1994)
Krippahl, L., Barahona, P.: Propagating N-ary rigid-body constraints. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 452–465. Springer, Heidelberg (2003)
Beck, J., Prosser, P., Wallace, R.: Toward understanding variable ordering heuristics for constraint satisfaction problems. In: Procs. of the Fourteenth Irish Artificial Intelligence and Cognitive Science Conference, AICS 2003 (2003)
Gent, I.P., MacIntyre, E., Prosser, P., Walsh, T.: The constrainedness of search. In: AAAI/IAAI, vol. 1, pp. 246–252 (1996)
Krippahl, L., Barahona, P.: PSICO: Solving protein structures with constraint programming and optimization. Constraints 7, 317–331 (2002)
Krippahl, L.: Integrating Protein Structural Information. PhD thesis, FCT/UNL (2003)
Seavey, B., Farr, E., Westler, W., Markley, J.: A relational database for sequencespecific protein nmr data. J. Biomolecular NMR 1, 217–236 (1991)
Noguchi, T., Onizuka, K., Akiyama, Y., Saito, M.: PDB-REPRDB: A database of representative protein chains in PDB (Protein Data Bank). In: Procs. of the 5th International Conference on Intelligent Systems for Molecular Biology, pp. 214–217. AAAI Press, Menlo Park (1997)
Correia, M.: Heuristic search for protein structure determination. Master’s thesis, FCT/UNL (Submitted March/2004)
Haykin, S.: Neural Networks: A comprehensive Foundation. Macmillan College Publishing Company, Inc., NYC (1994)
Harvey, W., Ginsberg, M.: Limited discrepancy search. In: Mellish, C. (ed.) IJCAI 1995 Procs. Int. Joint Conference on Artificial Intelligence, Montreal (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Correia, M., Barahona, P. (2004). Machine Learned Heuristics to Improve Constraint Satisfaction. In: Bazzan, A.L.C., Labidi, S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science(), vol 3171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28645-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-28645-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23237-7
Online ISBN: 978-3-540-28645-5
eBook Packages: Springer Book Archive