Skip to main content

Constraint Relationships for Soft Constraints

  • Conference paper
  • First Online:
Research and Development in Intelligent Systems XXX (SGAI 2013)

Abstract

We introduce constraint relationships as a means to define qualitative preferences on the constraints of soft constraint problems. The approach is aimed at constraint satisfaction problems (CSPs) with a high number of constraints that make exact preference quantizations hard to maintain manually or hard to anticipate—especially if constraints or preferences change at runtime or are extracted from natural language text. Modelers express preferences over the satisfaction of constraints with a clear semantics regarding preferred tuples without assigning priorities to concrete domain values. We show how a CSP including a set of constraint relationships can linearly be transformed into a k-weighted CSP as a representative of c-semirings that is solved by widely available constraint solvers and compare it with existing techniques. We demonstrate the approach by using a typical example of a dynamic and interactive scheduling problem in AI.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

References

  1. Andréka, H., Ryan, M., Schobbens, P.Y.: Operators and Laws for Combining Preference Relations. J. Log. Comput. 12(1), 13–53 (2002).

    Google Scholar 

  2. Bessière, C.: Arc-Consistency in Dynamic Constraint Satisfaction Problems. In: T.L. Dean, K. McKeown (eds.) Proc. \(9^\text{ th }\) Nat. Conf. Artificial Intelligence (AAAI’91), pp. 221–226. AAAI Press (1991).

    Google Scholar 

  3. Bistarelli, S., Montanari, U., Rossi, F.: Constraint Solving over Semirings. In: Proc. \(14^\text{ th }\) Int. Joint Conf. Artificial Intelligence (IJCAI’95), vol. 1, pp. 624–630. Morgan Kaufmann (1995).

    Google Scholar 

  4. Borning, A., Freeman-Benson, B., Wilson, M.: Constraint Hierarchies. LISP Symb. Comp. 5, 223–270 (1992).

    Google Scholar 

  5. Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements. J. Artif. Intell. Res. 21, 135–191 (2004).

    Google Scholar 

  6. Boutilier, C., Brafman, R.I., Geib, C., Poole, D.: A Constraint-based Approach to Preference Elicitation and Decision Making. In: AAAI Spring Symp. Qualitative Decision Theory, pp. 19–28 (1997).

    Google Scholar 

  7. Brafman, R., Domshlak, C.: Preference Handling - An Introductory Tutorial. AI Magazine 30(1), 58–86 (2009).

    Google Scholar 

  8. Dechter, R., Dechter, A.: Belief Maintenance in Dynamic Constraint Networks. In: H.E. Shrobe, T.M. Mitchell, R.G. Smith (eds.) Proc. \(7^\text{ th }\) Nat. Conf. Artificial Intelligence (AAAI’88), pp. 37–42. AAAI Press (1988).

    Google Scholar 

  9. Doyle, J., McGeachie, M.: Exercising Qualitative Control in Autonomous Adaptive Survivable Systems. In: Proc. \(2^\text{ nd }\) Int. Conf. Self-adaptive software: Applications (IWSAS’01), Lect. Notes Comp. Sci., vol. 2614, pp. 158–170. Springer (2003).

    Google Scholar 

  10. Dubois, D., Fargier, H., Prade, H.: The Calculus of Fuzzy Restrictions as a Basis for Flexible Constraint Satisfaction. In: Proc. \(2^\text{ nd }\) IEEE Int. Conf. Fuzzy Systems, vol. 2, pp. 1131–1136 (1993).

    Google Scholar 

  11. Freuder, E.C., Wallace, R.J.: Partial Constraint Satisfaction. Artif. Intell. 58(1–3), 21–70 (1992).

    Google Scholar 

  12. Gelain, M., Pini, M.S., Rossi, F., Venable, K.B.: Dealing with Incomplete Preferences in Soft Constraint Problems. In: C. Bessière (ed.) Proc. \(13^\text{ th }\) Int. Conf. Principles and Practice of Constraint Programming (CP’07), Lect. Notes Comp. Sci., vol. 4741, pp. 286–300. Springer (2007).

    Google Scholar 

  13. Jampel, M.: A Brief Overview of Over-constrained Systems. In: M. Jampel, E. Freuder, M. Maher (eds.) Over-Constrained Systems, Lect. Notes Comp. Sci., vol. 1106, pp. 1–22. Springer (1996).

    Google Scholar 

  14. Larrosa, J.: Node and Arc Consistency in Weighted CSP. In: R. Dechter, R.S. Sutton (eds.) Proc. \(18^\text{ th }\) Nat. Conf. Artificial Intelligence (AAAI’02), pp. 48–53. AAAI Press (2002).

    Google Scholar 

  15. Meseguer, P., Rossi, F., Schiex, T.: Soft Constraints. In: F. Rossi, P. van Beek, T. Walsh (eds.) Handbook of Constraint Programming, chap. 9. Elsevier (2006).

    Google Scholar 

  16. Mittal, S., Falkenhainer, B.: Dynamic Constraint Satisfaction. In: H.E. Shrobe, T.G. Dietterich, W.R. Swartout (eds.) Proc. \(8^\text{ th }\) Nat. Conf. Artificial Intelligence (AAAI’90), vol. 1, pp. 25–32. AAAI Press (1990).

    Google Scholar 

  17. Rossi, F.: Preferences, Constraints, Uncertainty, and Multi-Agent Scenarios. In: Proc. Int. Symp. Artificial Intelligence and Mathematics (ISAIM’08) (2008).

    Google Scholar 

  18. Rossi, F., Venable, K.B., Walsh, T.: Preferences in Constraint Satisfaction and Optimization. AI Mag. 29(4), 58–68 (2008).

    Google Scholar 

  19. Schiex, T., Fargier, H., Verfaillie, G.: Valued Constraint Satisfaction Problems: Hard and Easy Problems. In: Proc. \(14^\text{ th }\) Int. Joint Conf. Artificial Intelligence (IJCAI’95), vol. 1, pp. 631–639. Morgan Kaufmann (1995).

    Google Scholar 

  20. Shapiro, L.G., Haralick, R.M.: Structural descriptions and inexact matching. IEEE Trans. Pattern Analysis and, Machine Intelligence PAMI-3(5), 504–519 (1981).

    Google Scholar 

  21. Steghöfer, J.P., Anders, G., Siefert, F., Reif, W.: A System of Systems Approach to the Evolutionary Transformation of Power Management Systems. In: Proc. INFORMATIK 2013 Wsh. “Smart Grids”, Lect. Notes Inform. Bonner Köllen Verlag (2013).

    Google Scholar 

Download references

Acknowledgments

This work has been partially funded by the German Research Foundation (DFG) in the research unit FOR 1085 “OC Trust–Trustworthy Organic Computing Systems”. We would like to thank María Victoria Cengarle for fruitful discussions and the anonymous reviewers for the constructive feedback that led to an improvement of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexander Schiendorfer .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Schiendorfer, A., Steghöfer, JP., Knapp, A., Nafz, F., Reif, W. (2013). Constraint Relationships for Soft Constraints. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02621-3_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02620-6

  • Online ISBN: 978-3-319-02621-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics