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Robustness, stability, recoverability, and reliability in constraint satisfaction problems

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Robustness, stability, recoverability, and reliability in constraint satisfaction problems

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Barber Sanchís, F.; Salido Gregorio, MA. (2015). Robustness, stability, recoverability, and reliability in constraint satisfaction problems. Knowledge and Information Systems. 44(3):719-734. https://doi.org/10.1007/s10115-014-0778-3

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/68385

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Title: Robustness, stability, recoverability, and reliability in constraint satisfaction problems
Author: Barber Sanchís, Federico Salido Gregorio, Miguel Angel
UPV Unit: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería del Diseño - Escola Tècnica Superior d'Enginyeria del Disseny
Issued date:
Abstract:
Many real-world problems in Artificial Intelligence (AI) as well as in other areas of computer science and engineering can be efficiently modeled and solved using constraint programming techniques. In many real-world ...[+]
Subjects: Constraint satisfaction problems , Robustness , Stability , Dynamic CSPs
Copyrigths: Reserva de todos los derechos
Source:
Knowledge and Information Systems. (issn: 0219-1377 )
DOI: 10.1007/s10115-014-0778-3
Publisher:
Springer
Publisher version: http://link.springer.com/article/10.1007/s10115-014-0778-3
Project ID:
info:eu-repo/grantAgreement/MINECO//TIN2013-46511-C2-1-P/ES/TECNICAS INTELIGENTES PARA LA OBTENCION DE SOLUCIONES ROBUSTAS Y EFICIENTES ENERGETICAMENTE EN SCHEDULING: APLICACION AL TRANSPORTE::UPV/
Description: The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-014-0778-3
Thanks:
This work has been partially supported by the research project TIN2013-46511-C2-1 (MINECO, Spain). We would also thank the reviewers for their efforts and helpful comments.
Type: Artículo

References

Abril M, Barber F, Ingolotti L, Salido MA, Tormos P, Lova A (2008) An assessment of railway capacity. Transp Res Part E 44(5):774–806

Barber F (2000) Reasoning on intervals and point-based disjunctive metric constraints in temporal contexts. J Artif Intell Res 12:35–86

Bartak R, Salido MA (2011) Constraint satisfaction for planning and scheduling problems. Constraints 16(3):223–227 [+]
Abril M, Barber F, Ingolotti L, Salido MA, Tormos P, Lova A (2008) An assessment of railway capacity. Transp Res Part E 44(5):774–806

Barber F (2000) Reasoning on intervals and point-based disjunctive metric constraints in temporal contexts. J Artif Intell Res 12:35–86

Bartak R, Salido MA (2011) Constraint satisfaction for planning and scheduling problems. Constraints 16(3):223–227

Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35–53

Climent L, Wallace R, Salido M, Barber F (2013) Modeling robustness in CSPS as weighted CSPS. In: Integration of AI and OR techniques in constraint programming for combinatorial optimization problems CPAIOR 2013, pp 44–60

Climent L, Wallace R, Salido M, Barber F (2014) Robustness and stability in constraint programming under dynamism and uncertainty. J Artif Intell Res 49(1):49–78

Dechter R (1991) Temporal constraint network. Artif Intell 49:61–295

Hazewinkel M (2002) Encyclopaedia of mathematics. Springer, New York

Hebrard E (2007) Robust solutions for constraint satisfaction and optimisation under uncertainty. PhD thesis, University of New South Wales

Hebrard E, Hnich B, Walsh T (2004) Super solutions in constraint programming. In: Integration of AI and OR techniques in constraint programming for combinatorial optimization problems (CPAIOR-04), pp 157–172

Jen E (2003) Stable or robust? What’s the difference? Complexity 8(3):12–18

Kitano H (2007) Towards a theory of biological robustness. Mol Syst Biol 3(137)

Liebchen C, Lbbecke M, Mhring R, Stiller S (2009) The concept of recoverable robustness, linear programming recovery, and railway applications. In: LNCS, vol 5868

Papapetrou P, Kollios G, Sclaroff S, Gunopulos D (2009) Mining frequent arrangements of temporal intervals. Knowl Inf Syst 21:133–171

Rizk A, Batt G, Fages F, Solima S (2009) A general computational method for robustness analysis with applications to synthetic gene networks. Bioinformatics 25(12):168–179

Rossi F, van Beek P, Walsh T (2006) Handbook of constraint programming. Elsevier, New York

Roy B (2010) Robustness in operational research and decision aiding: a multi-faceted issue. Eur J Oper Res 200:629–638

Szathmary E (2006) A robust approach. Nature 439:19–20

Verfaillie G, Schiex T (1994) Solution reuse in dynamic constraint satisfaction problems. In: Proceedings of the 12th national conference on artificial intelligence (AAAI-94), pp 307–312

Wallace R, Grimes D, Freuder E (2009) Solving dynamic constraint satisfaction problems by identifying stable features. In: Proceedings of international joint conferences on artificial intelligence (IJCAI-09), pp 621–627

Wang D, Tse Q, Zhou Y (2011) A decentralized search engine for dynamic web communities. Knowl Inf Syst 26(1):105–125

Wiggins S (1990) Introduction to applied nonlinear dynamical systems and chaos. Springer, New York

Zhou Y, Croft W (2008) Measuring ranked list robustness for query performance prediction. Knowl Inf Syst 16:155–171

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