Skip to main content

Trying Again to Fail-First

  • Conference paper
Recent Advances in Constraints (CSCLP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3419))

Abstract

For constraint satisfaction problems (CSPs), Haralick & Elliott [1] introduced the Fail-First Principle and defined in it terms of minimizing branch depth. By devising a range of variable ordering heuristics, each in turn trying harder to fail first, Smith & Grant [2] showed that adherence to this strategy does not guarantee reduction in search effort. The present work builds on Smith & Grant. It benefits from the development of a new framework for characterizing heuristic performance that defines two policies, one concerned with enhancing the likelihood of correctly extending a partial solution, the other with minimizing the effort to prove insolubility. The Fail-First Principle can be restated as calling for adherence to the second, fail-first policy, while discounting the other, promise policy. Our work corrects some deficiencies in the work of Smith & Grant, and goes on to confirm their finding that the Fail-First Principle, as originally defined, is insufficient. We then show that adherence to the fail-first policy must be measured in terms of size of insoluble subtrees, not branch depth. We also show that for soluble problems, both policies must be considered in evaluating heuristic performance. Hence, even in its proper form the Fail-First Principle is insufficient. We also show that the “FF” series of heuristics devised by Smith & Grant is a powerful tool for evaluating heuristic performance, including the subtle relations between heuristic features and adherence to a policy.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Haralick, R.M., Elliott, G.L.: Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence 14, 263–314 (1980)

    Article  Google Scholar 

  2. Smith, B.M., Grant, S.A.: Trying harder to fail first. In: Proc. Thirteenth European Conference on Artificial Intelligence, ECAI 1998, pp. 249–253. John Wiley & Sons, Chichester (1998)

    Google Scholar 

  3. Beck, J.C., Prosser, P., Wallace, R.J.: Toward understanding variable ordering heuristics for constraint satisfaction problems. In: Proc. Fourteenth Irish Artificial Intelligence and Cognitive Science Conference, AICS 2003, pp. 11–16 (2003)

    Google Scholar 

  4. Beck, J.C., Prosser, P., Wallace, R.J.: Variable ordering heuristics show promise. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 711–715. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Geelen, P.A.: Dual viewpoint heuristics for binary constraint satisfaction problems. In: Proc. Tenth European Conference on Artificial Intelligence, ECAI 1992, pp. 31–35 (1992)

    Google Scholar 

  6. Brelaz, D.: New Methods to Color the Vertices of a Graph. Communications of the ACM 22, 251–256 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  7. Gent, I., MacIntyre, E., Prosser, P., Smith, B., Walsh, T.: An empirical study of dynamic variable ordering heuristics for the constraint satisfaction problem. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, pp. 179–193. Springer, Heidelberg (1996)

    Google Scholar 

  8. Beck, J.C., Prosser, P., Wallace, R.J.: Failing first: An update. In: Proc. Sixteenth European Conference on Artificial Intelligence, ECAI 2004, pp. 959–960 (2004)

    Google Scholar 

  9. Gent, I.P., MacIntyre, E., Prosser, P., Smith, B.M., Walsh, T.: Random constraint satisfaction: Flaws and structure. Constraints 6, 345–372 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  10. Gent, I.P., MacIntyre, E., Prosser, P., Walsh, T.: The constrainedness of search. In: Proc. Thirteenth National Conference on Artificial Intelligence, AAAI 1996, pp. 246–252 (1996)

    Google Scholar 

  11. Sabin, D., Freuder, E.: Contradicting Conventional Wisdom in Constraint Satisfaction. In: Proc. Eleventh European Conference on Artificial Intelligence, ECAI 1994, pp. 125–129. John Wiley & Sons, Chichester (1994)

    Google Scholar 

  12. Nudel, B.: Consistent-labeling problems and their algorithms: Expected-complexities and theory-based heuristics. Artificial Intelligence 21, 263–313 (1983)

    Article  Google Scholar 

  13. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)

    MATH  Google Scholar 

  14. Hays, W.L.: Statistics for the Social Sciences, 2nd edn. Holt, Rinehart, Winston (1973)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Beck, J.C., Prosser, P., Wallace, R.J. (2005). Trying Again to Fail-First. In: Faltings, B.V., Petcu, A., Fages, F., Rossi, F. (eds) Recent Advances in Constraints. CSCLP 2004. Lecture Notes in Computer Science(), vol 3419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11402763_4

Download citation

  • DOI: https://doi.org/10.1007/11402763_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25176-7

  • Online ISBN: 978-3-540-32252-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics