An efficient diagnosis algorithm for inconsistent constraint sets

Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM

Volumn 26, Issue 1, 2012, Pages 53-62

  Felfernig, A ^a   Schubert, M ^a   Zehentner, C ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Inconsistent Constraint Sets; Interactive Configuration; Model Based Diagnosis; Preferred Diagnosis

Indexed keywords

CONSTRAINT SET; DIAGNOSIS ALGORITHMS; ENGINEERING PHASE; HITTING SETS; INTERACTIVE CONFIGURATION; KNOWLEDGE BASE; MODEL BASED DIAGNOSIS; OVER-CONSTRAINED PROBLEM; PERFORMANCE ANALYSIS; SET OF CUSTOMERS; TEST CASE;

KNOWLEDGE BASED SYSTEMS;

LEARNING ALGORITHMS;

EID: 84856487299     PISSN: 08900604     EISSN: 14691760     Source Type: Journal    
DOI: 10.1017/S0890060411000011     Document Type: Article

References (29)

1. 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.
2. Belanger, F. (2005). A conjoint analysis of online consumer satisfaction. Journal of Electronic Commerce Research 6, 95-111.
3. Castillo, L., Borrajo, D., & Salido, M. (2005). Planning, Scheduling and Constraint Satisfaction: From Theory to Practice. Amsterdam: IOS Press.
4. Chen, Z., & Suen, C. (2003). Measuring the complexity of rule-based expert systems. Expert Systems with Applications 7(4), 467-481.
5. DeKleer, J. (1990). Using crude probability estimates to guide diagnosis. AI Journal 45(3), 381-391.
6. DeKleer, J., Mackworth, A., & Reiter, R. (1992). Characterizing diagnoses and systems. AI Journal 56(2-3), 197-222.
7. DeKleer, J., & Williams, B. (1987). Diagnosing multiple faults. AI Journal 32(1), 97-130.
8. 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.
9. 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.
10. Felfernig, A., Friedrich, G., Jannach, D., & Stumptner, M. (2004). Consistency-based diagnosis of configuration knowledge bases. AI Journal 152(2), 213-234.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. Jannach, D., & Liegl, J. (2006). Conflict-directed relaxation of constraints in content-based recommender systems. Proc. IEA/AIE 2006, pp. 819-829, Annency, France.
18. 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.
19. Lin, L., & Jiang, Y. (2002). Computing minimal hitting sets with genetic algorithms. Algorithmica 32(1), 95-106.
20. Lin, L., & Jiang, Y. (2003). The computation of hitting sets: Review and new algorithm. Information Processing Letters 86, 177-184.
21. 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.
22. 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.
23. 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.
24. Reiter, R. (1987). A theory of diagnosis from first principles. AI Journal 23(1), 57-95.
25. Siddiqi, S., & Huang, J. (2007). Hierarchical diagnosis of multiple faults. Proc. 20th Int. Joint Conf. Artificial Intelligence (IJCAI'07), pp. 581-586, Hyderabad, India.
26. Sinz, C., & Haag, A. (2007). Configuration. IEEE Intelligent Systems 22(1), 78-90.
27. Tsang, E. (1993). Foundations of Constraint Satisfaction. New York: Academic.
28. Winterfeldt, D., & Edwards, W. (1986). Decision Analysis and Behavioral Research. New York: Cambridge University Press.
29. Wotawa, F. (2001). A variant of Reiter's hitting-set algorithm. Information Processing Letters 79, 45-51.