Skip to main content
Log in

Constraint-based and SAT-based diagnosis of automotive configuration problems

  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

Abstract

We compare the concepts and computation of optimized diagnoses in the context of Boolean constraint based knowledge systems of automotive configuration, namely the preferred minimal diagnosis and the minimum weighted diagnosis. In order to restore the consistency of an over-constrained system w.r.t. a strict total order of the user requirements, the preferred minimal diagnosis tries to keep the most preferred user requirements and can be computed, for example, by the FASTDIAG algorithm. In contrast, partial weighted MinUNSAT solvers aim to find a set of unsatisfied clauses with the minimum sum of weights, such that the diagnosis is of minimum weight. It turns out that both concepts have similarities, i.e., both deliver an optimal minimal correction subset. We show use cases from automotive configuration where optimized diagnoses are desired. We point out theoretical commonalities and prove the reducibility of both concepts to each other, i.e., both problems are FPNP-complete, which was an open question. In addition to exact algorithms we present greedy algorithms. We evaluate the performance of exact and greedy algorithms on problem instances based on real automotive configuration data from three different German car manufacturers, and we compare the time and quality tradeoff.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. MiniSAT homepage: http://www.minisat.se

  2. PicoSAT homepage: http://fmv.jku.at/picosat

  3. OPENWBO is available at: http://sat.inesc-id.pt/open-wbo

  4. Eva500a is available at: http://www.maxsat.udl.cat/14/solvers/

  5. MaxSAT Competition 2014: http://www.maxsat.udl.cat/14/results/index.html

  6. msuncore is available at: http://logos.ucd.ie/web/doku.php?id=msuncore

  7. CPLEX is available at: http://www-01.ibm.com/software/commerce/optimization/ cplex-optimizer/

  8. MaxSAT Evaluations: http://www.maxsat.udl.cat

References

  • Ansótegui, C., Bonet, M.L., & Levy, J. (2009). Solving (weighted) partial MaxSAT through satisfiability testing, In Kullmann, O. (Ed.) SAT 2009, LNCS, vol. 5584, pp. 427–440. Springer Berlin Heidelberg.

  • Ansótegui, C., & Gabàs, J. (2013). Solving (weighted) partial MaxSAT with ILP, In Gomes, C.P., & Sellmann, M. (Eds.) CPAIOR 2013, LNCS, vol. 7874, pp. 403–409. Springer.

  • Argelich, J., Lynce, I., & Marques-Silva, J. (2009). On solving boolean multilevel optimization problems, In Boutilier, C. (Ed.) IJCAI 2009, pp. 393–398.

  • Argelich, J., & Manyà, F. (2006). Exact Max-SAT solvers for over-constrained problems. J Heuristics, 12(4–5), 375–392.

    Article  MATH  Google Scholar 

  • Audemard, G., Lagniez, J., & Simon, L. (2013). Improving glucose for incremental SAT solving with assumptions: Application to MUS extraction, In Järvisalo, M., & Gelder, A.V. (Eds.) SAT 2013, LNCS, vol. 7962, pp. 309–317. Springer.

  • Benavides, D., Segura, S., & Ruiz-Cortés, A. (2010). Automated analysis of feature models 20 years later: A literature review. Information Systems, 35(6), 615–636.

    Article  Google Scholar 

  • Biere, A. (2008). PicoSAT essentials. JSAT, 4(2–4), 75–97.

    MATH  Google Scholar 

  • Chen, Z., & Toda, S. (1995). The complexity of selecting maximal solutions. Inform. Comput, 119(2), 231–239.

    Article  MathSciNet  MATH  Google Scholar 

  • Cook, S.A. (1971). The complexity of theorem-proving procedures, In Harrison, M.A., Banerji, R.B., & Ullman, J.D. (Eds.) STOC, pp. 151–158. ACM.

  • Eén, N., & Sörensson, N. (2004). An extensible SAT-solver, In Giunchiglia, E., & Tacchella, A. (Eds.) SAT 2003, LNCS, vol. 2919, pp. 502–518. Springer Berlin Heidelberg.

  • Eén, N., & Sörensson, N. (2006). Translating pseudo-boolean constraints into SAT. JSAT, 2, 1–26.

    MATH  Google Scholar 

  • Felfernig, A., Schubert, M., & Zehentner, C. (2012). An efficient diagnosis algorithm for inconsistent constraint sets. AIEDAM, 26(1), 53–62.

    Article  Google Scholar 

  • Franco, J., & Martin, J. (2009). A history of satisfiability, In Biere, A., Heule, M., van Maaren, H., & Walsh, T. (Eds.) Handproceedings of Satisfiability, FAIA, vol. 185, chap. 1, pp. 3–74. IOS Press.

  • Fu, Z., & Malik, S. (2006). On solving the partial MAX-SAT problem, In Biere, A., & Gomes, C.P. (Eds.) SAT 2006, LNCS, vol. 4121, pp. 252–265. Springer.

  • Gottlob, G., & Fermüller, C.G. (1993). Removing redundancy from a clause. Artif. Intell, 61(2), 263–289.

    Article  MathSciNet  MATH  Google Scholar 

  • Heras, F., Morgado, A., & Marques-Silva, J. (2011). Core-guided binary search algorithms for maximum satisfiability. In Burgard, W., & Roth, D. (Eds.) AAAI, pp. 36–41. AAAI Press.

  • Heras, F., Morgado, A., & Marques-Silva, J. (2012). An empirical study of encodings for group MaxSAT. In Kosseim, L., & Inkpen, D. (Eds.) Canadian Conf. on AI, LNCS, vol. 7310, pp. 85–96. Springer.

  • Heras, F., Morgado, A., & Marques-Silva, J. (2012). Lower bounds and upper bounds for MaxSAT. In Hamadi, Y., & Schoenauer, M. (Eds.) LION 6, LNCS, vol. 7219, pp. 402–407. Springer Berlin Heidelberg.

  • Jenner, B., & Torán, J. (1995). Computing functions with parallel queries to NP. Theoretical Computer Science, 141(1–2), 175–193.

    Article  MathSciNet  MATH  Google Scholar 

  • Junker, U. (2004). QUICKXPLAIN: Preferred explanations and relaxations for over-constrained problems, In AAAI, pp. 167–172. AAAI Press / The MIT Press.

  • Krentel, M.W. (1988). The complexity of optimization problems. J. Comput. System Sci, 36(3), 490–509.

    Article  MathSciNet  MATH  Google Scholar 

  • Küchlin, W., & Sinz, C. (2000). Proving consistency assertions for automotive product data management. J. Automat. Reason, 24(1–2), 145–163.

    Article  MATH  Google Scholar 

  • Kügel, A. (2012). Improved exact solver for the weighted MAX-SAT problem, In Berre, D.L. (Ed.) POS-10. Pragmatics of SAT, EasyChair Proceedings in Computing, vol. 8, pp. 15–27. EasyChair.

  • Le Berre, D., & Parrain, A. (2010). The Sat4j library, release 2.2. JSAT, 7(2–3), 59–6.

    Google Scholar 

  • Li, C.M., & Manyà, F. (2009). MaxSAT, hard and soft constraints, In Biere, A., Heule, M., van Maaren, H., & Walsh, T. (Eds.) Handproceedings of Satisfiability, FAIA, vol. 185, chap. 19, pp. 613–631. IOS Press.

  • Liffiton, M.H., & Sakallah, K.A. (2008). Algorithms for computing minimal unsatisfiable subsets of constraints. J. Autom. Reasoning, 40(1), 1–33.

    Article  MathSciNet  MATH  Google Scholar 

  • Marques-Silva, J., Heras, F., Janota, M., Previti, A., & Belov, A. (2013). On computing minimal correction subsets, In Rossi, F. (Ed.) IJCAI, pp. 615–622, IJCAI/AAAI.

  • Marques-Silva, J., & Previti, A. (2014). On computing preferred MUSes and MCSes, In Sinz, C., & Egly, U. (Eds.) SAT 2014, LNCS, vol. 8561, pp. 58–74. Springer.

  • Martins, R., Manquinho, V.M., & Lynce, I. (2014). Open-WBO: A modular MaxSAT solver, In Sinz, C., & Egly, U. (Eds.) SAT 2014, LNCS, vol. 8561, pp. 438–445. Springer Int. Publishing.

  • Mencía, C., & Marques-Silva, J. (2014). Efficient relaxations of over-constrained CSPs, In ICTAI 2014, pp. 725–732. IEEE.

  • Morgado, A., Heras, F., Liffiton, M.H., Planes, J., & Marques-Silva, J. (2013). Iterative and core-guided MaxSAT solving: A survey and assessment. Constraints, 18 (4), 478–534.

    Article  MathSciNet  MATH  Google Scholar 

  • Narodytska, N., & Bacchus, F. (2014). Maximum satisfiability using core-guided maxsat resolution, In Brodley, C.E., & Stone, P. (Eds.) AAAI, pp. 2717–2723. AAAI Press.

  • O’Callaghan, B., O’Sullivan, B., & Freuder, E.C. (2005). Generating corrective explanations for interactive constraint satisfaction, In van Beek, P. (Ed.) CP 2005, LNCS, vol. 3709, pp. 445–459. Springer.

  • Papadimitriou, C.M. (1994). Computational complexity. Addison-Wesley. Massachusetts: Reading.

  • Plaisted, D.A., & Greenbaum, S. (1986). A structure-preserving clause form translation. J. Symbolic Comput, 2(3), 293–304.

    Article  MathSciNet  MATH  Google Scholar 

  • Schrijver, A. (1998). Theory of linear and integer programming: Wiley-Interscience.

  • Selman, A.L. (1994). A taxonomy of complexity classes of functions. J. Comput. Syst. Sci, 48(2), 357–381.

    Article  MathSciNet  MATH  Google Scholar 

  • Sinz, C. (2005). Towards an optimal CNF encoding of boolean cardinality constraints, In van Beek, P. (Ed.) CP 2005, LNCS, vol. 3709, pp. 827–831. Springer.

  • Sinz, C., Kaiser, A., & Küchlin, W. (2003). Formal methods for the validation of automotive product configuration data. AIEDAM, 17(1), 75–97.

    Article  Google Scholar 

  • Tseitin, G.S. (1970). On the complexity of derivations in the propositional calculus. Studies in Constructive Mathematics and Mathematical Logic Part II, 115–125.

  • Walter, R., Felfernig, A., & Küchlin, W. (2015). Inverse QuickXPlain vs. MaxSAT — a comparison in theory and practice. In Tiihonen, J., Falkner, A., & Axling, T. (Eds.) Proc. of the 17th Int. Config. Workshop, pp. 97–104. Vienna, Austria.

  • Walter, R., & Küchlin, W. (2014). ReMax – a MaxSAT aided product configurator, In Felfernig, A., Forza, C., & Haag, A. (Eds.) Proc. of the 16th Int. Config. Workshop, pp. 59–66. Novi Sad, Serbia.

  • Walter, R., Zengler, C., & Küchlin, W. (2013). Applications of MaxSAT in automotive configuration, In Aldanondo, M., & Falkner, A. (Eds.) Proc. of the 15th Int. Config. Workshop, pp. 21–28. Vienna, Austria.

  • Warners, J.P. (1998). A linear-time transformation of linear inequalities into conjunctive normal form. Information Processing Letters, 68(2), 63–69.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rouven Walter.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Walter, R., Felfernig, A. & Küchlin, W. Constraint-based and SAT-based diagnosis of automotive configuration problems. J Intell Inf Syst 49, 87–118 (2017). https://doi.org/10.1007/s10844-016-0422-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10844-016-0422-7

Keywords

Navigation