Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-18T19:59:08.206Z Has data issue: false hasContentIssue false
  • English
  • Français

Personalized diagnoses for inconsistent user requirements

Published online by Cambridge University Press:  20 April 2011

Alexander Felfernig  and
Monika Schubert
Alexander Felfernig
Affiliation:
Institute for Software Technology, Graz University of Technology, Graz, Austria
Monika Schubert
Affiliation:
Institute for Software Technology, Graz University of Technology, Graz, Austria
Get access Rights & Permissions [Opens in a new window]

Abstract

Knowledge-based configurators are supporting configuration tasks for complex products such as telecommunication systems, computers, or financial services. Product configurations have to fulfill the requirements articulated by the user and the constraints contained in the configuration knowledge base. If the user requirements are inconsistent with the constraints in the configuration knowledge base, users have to be supported in finding out a way from the no solution could be found dilemma. In this paper we introduce a new algorithm (PersDiag) that allows the determination of personalized diagnoses for inconsistent user requirements in knowledge-based configuration scenarios. We present the results of an empirical study that show the advantages of our approach in terms of prediction quality and efficiency.

Keywords

ConfigurationModel-Based DiagnosisPersonalization
Type
Special Issue Articles
Information
AI EDAM , Volume 25 , Issue 2: Configuration , May 2011 , pp. 175 - 183
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Barker, V., O'Connor, D., & Soloway, E. (1989). Expert systems for configuration at digital—XCON and beyond. Communications of the ACM 32(3), 298–318.CrossRefGoogle Scholar
Burke, R. (2000). Knowledge-based recommender systems. Library and Information Systems 69(32), 180–200.Google Scholar
DeKleer, J., Mackworth, A., & Reiter, R. (1992). Characterizing diagnoses and systems. AI Journal 56(2–3), 197–222.Google 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., Jannach, D., Stumptner, M., & Zanker, M. (2003). Configuration knowledge representations for semantic web applications. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 17(2), 31–50.CrossRefGoogle Scholar
Felfernig, A., Friedrich, G., & Schmidt-Thieme, L. (2007). Introduction to the IEEE intelligent systems special issue: recommender systems. IEEE Intelligent Systems 22(3), 18–21.CrossRefGoogle 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 (IJCAI09), pp. 791–796, Pasadena, CA.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., Gottlob, G., & Neijdl, W. (1990). Physical impossibility instead of fault models. Proc. 8th National Conf. Artificial Intelligence AAAI/IAAI90, pp. 331–336, Boston.Google Scholar
Friedrich, G., Stumptner, M., & Wotawa, F. (1999). Model-based diagnosis of hardware designs. Artificial Intelligence 111(2), 3–39.CrossRefGoogle Scholar
Godfrey, P. (1997). Minimization in cooperative response to failing database queries. International Journal of Cooperative Information Systems 6(2), 95–149.CrossRefGoogle Scholar
Haag, A. (1998). Sales configuration in business processes. IEEE Intelligent Systems 13(4), 78–85.CrossRefGoogle Scholar
Junker, U. (2004). QuickXPlain: preferred explanations and relaxations for over-constrained problems. Proc. 19th National Conf. Artificial Intelligence (AAAI04), pp. 167–172, San Jose, CA.Google Scholar
Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., & Riedl, J. (1997). Grouplens: applying collaborative filtering to usenet news. Communications of the ACM 40(3), 77–87.CrossRefGoogle Scholar
McDermott, J. (1982). R1—a rule-based configurer of computer systems. Artificial Intelligence 19(1), 39–88.CrossRefGoogle Scholar
McSherry, D. (2004). Maximally successful relaxations of unsuccessful queries. Proc. 15th Conf. Artificial Intelligence and Cognitive Science, pp. 127–136, Galway, Ireland.Google Scholar
McSherry, D. (2005). Retrieval failure and recovery in recommender systems. Artificial Intelligence Review 24(3–4), 319–338.CrossRefGoogle Scholar
Mittal, S., & Falkenhainer, B. (1990). Dynamic constraint satisfaction problems. Proc. 8th National Conf. Artificial Intelligence, IAAI/AAAI90, pp. 25–32, Boston.Google Scholar
Mittal, S., & Frayman, F. (1989). Towards a generic model of configuration tasks. Proc. 11th Int. Joint Conf. Artificial Intelligence (IJCAI89), pp. 1395–1401, Detroit, MI.Google Scholar
Orsvarn, K. (2005). Tacton configurator—research directions. Proc. IJCAI 2005 Workshop on Configuration, p. 75, Edinburgh, Scotland.Google Scholar
O'Sullivan, B., Papdopoulos, A., Faltings, B., & Pu, P. (2007). Representative explanations for over-constrained problems. Proc. 22nd National Conf. Artificial Intelligence (AAAI07), 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
Sabin, D., & Weigel, R. (1998). Product configuration frameworks—a survey. IEEE Intelligent Systems 13(4), 42–49.CrossRefGoogle Scholar
Sachenbacher, M., Struss, P., & Carlen, C. (2000). Prototype for model-based on-board diagnosis of automotive systems. AI Communications 13(2), 83–97.Google Scholar
Schubert, M., Felfernig, A., & Mandl, M. (2009). Solving over-constrained problems using network analysis. Proc. Int. Conf. Adaptive and Intelligent Systems, pp. 9–14, Klagenfurt, Austria.Google Scholar
Schubert, M., Felfernig, A., & Mandl, M. (2010). Fastxplain: conflict detection for constraint-based recommender problems. Proc. 23rd Int. Conf. Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 621–630, Cordoba, Spain.Google Scholar
Sinz, C., & Haag, A. (2007). Configuration. IEEE Intelligent Systems 22(1), 78–90.CrossRefGoogle Scholar
Stumptner, M. (1997). An overview of knowledge-based configuration. AI Communications 10(2), 111–125.Google Scholar
Tsang, E. (1993). Foundations of Constraint Satisfaction. Reading, MA: Academic Press.Google Scholar
Wilson, D., & Martinez, T. (1997). Improved heterogeneous distance functions. Journal of Artificial Intelligence Research, 6, 1–34.CrossRefGoogle Scholar
Winterfeldt, D., & Edwards, W. (1986). Decision Analysis and Behavioral Research. Cambridge: Cambridge University Press.Google Scholar