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An efficient diagnosis algorithm for inconsistent constraint sets

Published online by Cambridge University Press:  10 June 2011

A. Felfernig ,
M. Schubert  and
C. Zehentner
A. Felfernig*
Affiliation:
Institute for Software Technology, Graz University of Technology, Graz, Austria
M. Schubert
Affiliation:
Institute for Software Technology, Graz University of Technology, Graz, Austria
C. Zehentner
Affiliation:
Institute for Software Technology, Graz University of Technology, Graz, Austria
*
Reprint requests to: A. Felfernig, Institute for Software Technology, Graz University of Technology, Inffeldgasse 16b, A-8010 Graz, Austria. E-mail: alexander.felfernig@ist.tugraz.at
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Abstract

Constraint sets can become inconsistent in different contexts. For example, during a configuration session the set of customer requirements can become inconsistent with the configuration knowledge base. Another example is the engineering phase of a configuration knowledge base where the underlying constraints can become inconsistent with a set of test cases. In such situations we are in the need of techniques that support the identification of minimal sets of faulty constraints that have to be deleted in order to restore consistency. In this paper we introduce a divide and conquer-based diagnosis algorithm (FastDiag) that identifies minimal sets of faulty constraints in an overconstrained problem. This algorithm is specifically applicable in scenarios where the efficient identification of leading (preferred) diagnoses is crucial. We compare the performance of FastDiag with the conflict-directed calculation of hitting sets and present an in-depth performance analysis that shows the advantages of our approach.

Keywords

Inconsistent Constraint SetsInteractive ConfigurationModel-Based DiagnosisPreferred Diagnosis

Information

Type
Articles
Information
AI EDAM , Volume 26 , Issue 1 , February 2012 , pp. 53 - 62
Copyright
Copyright © Cambridge University Press 2012

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References

REFERENCES

Ardissono, L., Felfernig, A., Friedrich, G., Jannach, D., Petrone, G., Schaefer, R., & Zanker, M. (2003). A framework for the development of personalized, distributed web-based configuration systems. AI Magazine 24(3), 93–108.Google Scholar
Belanger, F. (2005). A conjoint analysis of online consumer satisfaction. Journal of Electronic Commerce Research 6, 95–111.Google Scholar
Castillo, L., Borrajo, D., & Salido, M. (2005). Planning, Scheduling and Constraint Satisfaction: From Theory to Practice. Amsterdam: IOS Press.Google Scholar
Chen, Z., & Suen, C. (2003). Measuring the complexity of rule-based expert systems. Expert Systems with Applications 7(4), 467–481.CrossRefGoogle Scholar
DeKleer, J. (1990). Using crude probability estimates to guide diagnosis. AI Journal 45(3), 381–391.Google Scholar
DeKleer, J., Mackworth, A., & Reiter, R. (1992). Characterizing diagnoses and systems. AI Journal 56(2–3), 197–222.Google Scholar
DeKleer, J., & Williams, B. (1987). Diagnosing multiple faults. AI Journal 32(1), 97–130.Google Scholar
Feldman, A., Provan, G., & Gemund, A. (2008). Computing minimal diagnoses by greedy stochastic search. Proc. 23rd AAAI Conf. Artificial Intelligence (AAAI’08), pp. 911–918, Chicago.Google Scholar
Felfernig, A., Friedrich, G., Isak, K., Shchekotykhin, K., Teppan, E., & Jannach, D. (2007). Automated debugging of recommender user interface descriptions. Journal of Applied Intelligence 31(1), 1–14.CrossRefGoogle Scholar
Felfernig, A., Friedrich, G., Jannach, D., & Stumptner, M. (2004). Consistency-based diagnosis of configuration knowledge bases. AI Journal 152(2), 213–234.Google Scholar
Felfernig, A., Friedrich, G., Schubert, M., Mandl, M., Mairitsch, M., & Teppan, E. (2009). Plausible repairs for inconsistent requirements. Proc. 21st Int. Joint Conf. Artificial Intelligence (IJCAI’09), pp. 791–796, Pasadena, CA.Google Scholar
Felfernig, A., Friedrich, G., Teppan, E., & Isak, K. (2008). Intelligent debugging and repair of utility constraint sets in knowledge-based recommender applications. Proc. 13th ACM Int. Conf. Intelligent User Interfaces (IUI’08), pp. 218–226, Canary Islands, Spain.CrossRefGoogle Scholar
Fijany, A., & Vatan, F. (2004). New approaches for efficient solutions of hitting set problems. Proc. Int. Symp. Information and Communication Technologies, pp. 1–10, Cancun, Mexico.Google Scholar
Fleischanderl, G., Friedrich, G., Haselboeck, A., Schreiner, H., & Stumptner, M. (1998). Configuring large systems using generative constraint satisfaction. IEEE Intelligent Systems 13(4), 59–68.CrossRefGoogle Scholar
Friedrich, G., & Shchekotykhin, K. (2005). A general diagnosis method for ontologies. Proc. 4th Int. Semantic Web Conference (ISWC’05), LNCS, Vol. 3729, pp. 232–246. New York: Springer.Google Scholar
Fröhlich, P., Nejdl, W., & Schroeder, M. (1994). A formal semantics for preferences and strategies in model-based diagnosis. Proc. 5th Int. Workshop on Principles of Diagnosis (DX-94), pp. 106–113.Google Scholar
Jannach, D., & Liegl, J. (2006). Conflict-directed relaxation of constraints in content-based recommender systems. Proc. IEA/AIE 2006, pp. 819–829, Annency, France.CrossRefGoogle Scholar
Junker, U. (2004). QuickXplain: preferred explanations and relaxations for over-constrained problems. Proc. 19th National Conf. Artificial Intelligence (AAAI’04), pp. 167–172, San Jose, CA.Google Scholar
Lin, L., & Jiang, Y. (2002). Computing minimal hitting sets with genetic algorithms. Algorithmica 32(1), 95–106.Google Scholar
Lin, L., & Jiang, Y. (2003). The computation of hitting sets: review and new algorithm. Information Processing Letters 86, 177–184.CrossRefGoogle Scholar
Marques-Silva, J., & Sakallah, K. (1996). Grasp: a new search algorithm for satisfiability. Proc. Int. Conf. Computer-Aided Design, pp. 220–227, Santa Clara, CA.CrossRefGoogle Scholar
Mittal, S., & Frayman, F. (1989). Towards a generic model of configuration tasks. Proc. 11th Int. Joint Conf. Artificial Intelligence (IJCAI’89), pp. 1395–1401, Detroit, MI.Google Scholar
O'Sullivan, B., Papdopoulos, A., Faltings, B., & Pu, P. (2007). Representative explanations for over-constrained problems. Proc. 22nd National Conf. Artificial Intelligence (AAAI’07), pp. 323–328, Vancouver, Canada.Google Scholar
Reiter, R. (1987). A theory of diagnosis from first principles. AI Journal 23(1), 57–95.Google Scholar
Siddiqi, S., & Huang, J. (2007). Hierarchical diagnosis of multiple faults. Proc. 20th Int. Joint Conf. Artificial Intelligence (IJCAI’07), pp. 581–586, Hyderabad, India.Google Scholar
Sinz, C., & Haag, A. (2007). Configuration. IEEE Intelligent Systems 22(1), 78–90.CrossRefGoogle Scholar
Tsang, E. (1993). Foundations of Constraint Satisfaction. New York: Academic.Google Scholar
Winterfeldt, D., & Edwards, W. (1986). Decision Analysis and Behavioral Research. New York: Cambridge University Press.Google Scholar
Wotawa, F. (2001). A variant of Reiter's hitting-set algorithm. Information Processing Letters 79, 45–51.CrossRefGoogle Scholar