Skip to main content
SpringerLink
Log in

Solving Vehicle Equipment Specification Problems with Answer Set Programming

  • Conference paper
  • First Online:
Practical Aspects of Declarative Languages (PADL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13880))

Included in the following conference series:

  • 334 Accesses

Abstract

We develop an approach to solving mono- and multi-objective vehicle equipment specification problems considering the corporate average fuel economy standard (CAFE problems, in short) in automobile industry. Our approach relies upon Answer Set Programming (ASP). The resulting system aspcafe accepts a CAFE instance expressed in the orthogonal variability model format and converts it into ASP facts. In turn, these facts are combined with an ASP encoding for CAFE solving, which can subsequently be solved by any off-the-shelf ASP systems. To show the effectiveness of our approach, we conduct experiments using a benchmark set based on real data provided by a collaborating Japanese automaker.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    By demand of our collaborating automaker, we use a simple CAFE standard.

  2. 2.

    For simplicity, the number of equipment options is limited to exactly one, but can be easily modified to arbitrary bounds for multiplicity.

  3. 3.

    All source code is available from https://github.com/banbaralab/aspcafe.

References

  1. Alviano, M., Dodaro, C.: Anytime answer set optimization via unsatisfiable core shrinking. Theory Pract. Logic Program. 16(5–6), 533–551 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  2. Amilhastre, J., Fargier, H., Marquis, P.: Consistency restoration and explanations in dynamic CSPs application to configuration. Artif. Intell. 135(1–2), 199–234 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Andres, B., Kaufmann, B., Matheis, O., Schaub, T.: Unsatisfiability-based optimization in clasp. In: Dovier, A., Santos Costa, V. (eds.) Technical Communications of the Twenty-Eighth International Conference on Logic Programming (ICLP 2012), vol. 17, pp. 212–221. LIPIcs (2012)

    Google Scholar 

  4. Aschinger, M., Drescher, C., Gottlob, G., Vollmer, H.: Loco - A logic for configuration problems. ACM Trans. Comput. Logic 15(3), 20:1–20:25 (2014)

    Google Scholar 

  5. Balduccini, M., Lierler, Y.: Integration schemas for constraint answer set programming: a case study. Theory Pract. Logic Program. 13(4-5-Online-Supplement) (2013)

    Google Scholar 

  6. Banbara, M., Kaufmann, B., Ostrowski, M., Schaub, T.: Clingcon: the next generation. Theory Pract. Logic Program. 17(4), 408–461 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  7. Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)

    Book  MATH  Google Scholar 

  8. Brewka, G., Delgrande, J., Romero, J., Schaub, T.: asprin: customizing answer set preferences without a headache. In: Bonet, B., Koenig, S. (eds.) Proceedings of the Twenty-Ninth National Conference on Artificial Intelligence (AAAI 2015), pp. 1467–1474. AAAI Press (2015)

    Google Scholar 

  9. Cabalar, P., Kaminski, R., Schaub, T., Schuhmann, A.: Temporal answer set programming on finite traces. Theory Pract. Logic Program. 18(3–4), 406–420 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  10. Calimeri, F., et al.: ASP-Core-2: input language format (2012)

    Google Scholar 

  11. Dodaro, C., Gasteiger, P., Leone, N., Musitsch, B., Ricca, F., Schekotihin, K.: Combining answer set programming and domain heuristics for solving hard industrial problems. Theory Pract. Logic Program. 16(5–6), 653–669 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  12. Ehrgott, M.: Multicriteria Optimization. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  13. Erdem, E., Gelfond, M., Leone, N.: Applications of ASP. AI Mag. 37(3), 53–68 (2016)

    Google Scholar 

  14. Friedrich, G., Ryabokon, A., Falkner, A.A., Haselböck, A., Schenner, G., Schreiner, H.: (Re)configuration using answer set programming. In: Shchekotykhin, K.M., Jannach, D., Zanker, M. (eds.) Proceedings of the IJCAI 2011 Workshop on Configuration. CEUR Workshop Proceedings, vol. 755. CEUR-WS.org (2011)

    Google Scholar 

  15. Gebser, M., et al.: Potassco User Guide. second edition. University of Potsdam (2015). http://potassco.org

  16. Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer Set Solving in Practice. Morgan and Claypool Publishers, San Rafael (2012)

    MATH  Google Scholar 

  17. Gebser, M., Kaufmann, B., Otero, R., Romero, J., Schaub, T., Wanko, P.: Domain-specific heuristics in answer set programming. In: desJardins, M., Littman, M. (eds.) Proceedings of the Twenty-Seventh National Conference on Artificial Intelligence (AAAI 2013), pp. 350–356. AAAI Press (2013)

    Google Scholar 

  18. Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Multi-shot ASP solving with clingo. Theory Pract. Logic Program. 19(1), 27–82 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  19. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Kowalski, R., Bowen, K. (eds.) Proceedings of the Fifth International Conference and Symposium of Logic Programming (ICLP 1988), pp. 1070–1080. MIT Press (1988)

    Google Scholar 

  20. Gençay, E., Schüller, P., Erdem, E.: Applications of non-monotonic reasoning to automotive product configuration using answer set programming. J. Intell. Manuf. 30(3), 1407–1422 (2019)

    Article  Google Scholar 

  21. Küchlin, W., Sinz, C.: Proving consistency assertions for automotive product data management. J. Autom. Reason. 24(1/2), 145–163 (2000)

    Article  MATH  Google Scholar 

  22. National Highway Traffic Safety Administration: Corporate average fuel economy. https://www.nhtsa.gov/laws-regulations/corporate-average-fuel-economy

  23. Niemelä, I.: Logic programs with stable model semantics as a constraint programming paradigm. Ann. Math. Artif. Intell. 25(3–4), 241–273 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  24. Pohl, K., Böckle, G., van der Linden, F.: Software Product Line Engineering: Foundations. Principles and Techniques. Springer, Heidelberg (2005)

    Google Scholar 

  25. Shiau, C.S.N., Michalek, J.J., Hendrickson, C.T.: A structural analysis of vehicle design responses to corporate average fuel economy policy. Transp. Res. Part A: Policy Pract. 43(9–10), 814–828 (2009)

    Google Scholar 

  26. Simons, P., Niemelä, I., Soininen, T.: Extending and implementing the stable model semantics. Artif. Intell. 138(1–2), 181–234 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  27. Sinz, C., Blochinger, W., Küchlin, W.: PaSAT - parallel SAT-checking with lemma exchange: implementation and applications. Electron. Notes Discrete Math. 9, 205–216 (2001)

    Article  MATH  Google Scholar 

  28. Sinz, C., Kaiser, A., Küchlin, W.: Detection of inconsistencies in complex product configuration data using extended propositional SAT-checking. In: Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference, pp. 645–649. AAAI Press (2001)

    Google Scholar 

  29. Sinz, C., Kaiser, A., Küchlin, W.: Formal methods for the validation of automotive product configuration data. Artif. Intell. Eng. Des. Anal. Manuf. 17(1), 75–97 (2003)

    Article  Google Scholar 

  30. Soh, T., Banbara, M., Tamura, N., Le Berre, D.: Solving multiobjective discrete optimization problems with propositional minimal model generation. In: Beck, J.C. (ed.) CP 2017. LNCS, vol. 10416, pp. 596–614. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66158-2_38

    Chapter  Google Scholar 

  31. Soininen, T., Niemelä, I., Tiihonen, J., Sulonen, R.: Representing configuration knowledge with weight constraint rules. In: Provetti, A., Son, T.C. (eds.) Proceedings of the AAAI Spring 2001 Symposium on Answer Set Programming, pp. 195–201. AAAI Press (2001)

    Google Scholar 

  32. Tiihonen, J., Heiskala, M., Anderson, A., Soininen, T.: Wecotin - A practical logic-based sales configurator. AI Commun. 26(1), 99–131 (2013)

    Article  MathSciNet  Google Scholar 

  33. Wakaki, T., Inoue, K., Sakama, C., Nitta, K.: Computing preferred answer sets in answer set programming. In: Vardi, M.Y., Voronkov, A. (eds.) LPAR 2003. LNCS (LNAI), vol. 2850, pp. 259–273. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39813-4_18

    Chapter  MATH  Google Scholar 

  34. Walter, R., Felfernig, A., Küchlin, W.: Constraint-based and SAT-based diagnosis of automotive configuration problems. J. Intell. Inf. Syst. 49(1), 87–118 (2017)

    Article  Google Scholar 

  35. Walter, R., Küchlin, W.: Remax - A MaxSAT aided product (re-)configurator. In: Proceedings of the Sixteenth International Configuration Workshop. pp. 59–66. CEUR Workshop Proceedings, CEUR-WS.org (2014)

    Google Scholar 

  36. Walter, R., Zengler, C., Küchlin, W.: Applications of MaxSAT in automotive configuration. In: Proceedings of the Fifteenth International Configuration Workshop. pp. 21–28. CEUR Workshop Proceedings, CEUR-WS.org (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan

    Raito Takeuchi & Mutsunori Banbara

  2. Kobe University, Rokko-dai Nada-ku Kobe Hyogo, 657-8501, Japan

    Naoyuki Tamura

  3. Universität Potsdam, August-Bebel-Strasse, 14482, Potsdam, Germany

    Torsten Schaub

Authors
  1. Raito Takeuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mutsunori Banbara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Naoyuki Tamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Torsten Schaub
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raito Takeuchi .

Editor information

Editors and Affiliations

  1. University of Kiel, Kiel, Germany

    Michael Hanus

  2. Miami University, Oxford, OH, USA

    Daniela Inclezan

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Takeuchi, R., Banbara, M., Tamura, N., Schaub, T. (2023). Solving Vehicle Equipment Specification Problems with Answer Set Programming. In: Hanus, M., Inclezan, D. (eds) Practical Aspects of Declarative Languages. PADL 2023. Lecture Notes in Computer Science, vol 13880. Springer, Cham. https://doi.org/10.1007/978-3-031-24841-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-24841-2_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24840-5

  • Online ISBN: 978-3-031-24841-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation