Advertisement

Heuristic Perimeter Search: First Results

  • Carlos Linares López
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)

Abstract

Since its conception, the perimeter idea has been understood as a mean for boosting single-agent search algorithms when solving different problems with the same target node, t. However, various results emphasize that the most remarkable contribution of perimeter search is that it is an efficient way for improving the original heuristic estimations. Henceforth, a natural question arises: whether it is feasible or not to increase even more the capabilities for improving h (·) when using a perimeter-like approach. As it will be shown, the so-called “heuristic perimeter” idea can be widely considered as an alternative to the classical perimeter and as a baseline for the research in this area.

Keywords

Search Algorithm Test Suite Target Node Heuristic Function Forward Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pearl, J.: Heuristics. Addison-Wesley, Reading (1984)Google Scholar
  2. 2.
    Korf, R.E., Reid, M., Edelkamp, S.: Time complexity of iterative-deepening-A ⋆ . Artificial Intelligence 129(1–2), 199–218 (2001)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Hansson, O., Mayer, A., Yung, M.: Criticizing solutions to relaxed models yields powerful admissible heuristics. Information Sciences 63, 207–222 (1992)CrossRefGoogle Scholar
  4. 4.
    Korf, R.E., Taylor, L.A.: Finding optimal solutions to the twenty-four puzzle. In: Proceedings AAAI, vol. 96, pp. 1202–1207 (1996)Google Scholar
  5. 5.
    Culberson, J.C., Schaeffer, J.: Searching with pattern databases. In: Advances in Artificial Intelligence, pp. 402–416. Springer, Heidelberg (1996)Google Scholar
  6. 6.
    Korf, R.E., Felner, A.: Disjoint pattern database heuristics. Artificial Intelligence 134, 9–22 (2002)MATHCrossRefGoogle Scholar
  7. 7.
    Auer, A., Kaindl, H.: A case study of revisiting best-first vs. depth-first search. In: Proceedings ECAI 2004, Valencia, Spain, pp. 141–145 (2004)Google Scholar
  8. 8.
    Kaindl, H., Kainz, G.: Bidirectional heuristic search reconsidered. Journal of Artificial Intelligence Research 7, 283–317 (1997)MATHMathSciNetGoogle Scholar
  9. 9.
    Manzini, G.: BIDA ⋆ : an improved perimeter search algorithm. Artificial Intelligence 75, 347–360 (1995)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Dillenburg, J.F., Nelson, P.C.: Perimeter search. Artificial Intelligence 65, 165–178 (1994)CrossRefGoogle Scholar
  11. 11.
    Korf, R.E.: Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence 27, 97–109 (1985)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Linares, C., Junghanns, A.: Perimeter Search Performance. In: Schaeffer, J., Müller, M., Björnsson, Y. (eds.) CG 2002. LNCS, vol. 2883, pp. 345–359. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Linares, C.: On the Heuristic Performance of Perimeter Search Algorithms. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, J.-L. (eds.) CAEPIA/TTIA 2003. LNCS (LNAI), vol. 3040, pp. 445–456. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Carlos Linares López
    • 1
  1. 1.Planning and Learning Group, Computer Science DepartmentUniversidad Carlos III de MadridLeganés, MadridSpain

Personalised recommendations