Advertisement

Similarity Pruning in PrOM Search

  • H. (Jeroen) H. L. M. Donkers
  • H. Jaap van den Herik
  • Jos W. H. M. Uiterwijk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4250)

Abstract

In this paper we introduce a new pruning mechanism, called Similarity Pruning for Probabilistic Opponent-Model (PrOM) Search. It is based on imposing a bound on the differences between two or more evaluation functions. Assuming such a bound exists, we are able to prove two theoretical properties, viz., the bound-conservation property and the bounded-gain property. Using these properties we develop a Similarity-Pruning algorithm. Subsequently we conduct a series of experiments on random game trees to measure the efficiency of the new algorithm. The results show that Similarity Pruning increases the efficiency of PrOM search considerably.

Keywords

Evaluation Function Leaf Node Search Tree Child Node Game Tree 
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.
    Carmel, D., Markovitch, S.: Learning and Using Opponent Models in Adversary Search. Technical Report CIS9609, Technion, Haifa, Israel (1996)Google Scholar
  2. 2.
    Carmel, D., Markovitch, S.: Pruning Algorithms for Multi-Model Adversary Search. Artificial Intelligence 99(2), 325–355 (1998)MATHCrossRefGoogle Scholar
  3. 3.
    Donkers, H.H.L.M.: Nosce Hostem: Searching with Opponent Models. PhD thesis, Universiteit Maastricht. Universitaire Pers Maastricht, Maastricht, The Netherlands (2003)Google Scholar
  4. 4.
    Donkers, H.H.L.M., Uiterwijk, J.W.H.M., van den Herik, H.J.: Probabilistic Opponent-Model Search. Information Sciences 135(3–4), 123–149 (2001)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Donkers, H.H.L.M., van den Herik, H.J., Uiterwijk, J.W.H.M.: Probabilistic Opponent-Model Search in Bao. In: Rauterberg, M. (ed.) ICEC 2004. LNCS, vol. 3166, pp. 409–419. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Iida, H., Uiterwijk, J.W.H.M., van den Herik, H.J., Herschberg, I.S.: Potential Applications of Opponent-Model Search. Part 1: the Domain of Applicability. ICCA Journal 16(4), 201–208 (1993)Google Scholar
  7. 7.
    Korf, R.E.: Multi-Player Alpha-Beta Pruning. Artificial Intelligence 48(1), 99–111 (1991)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Luckhardt, C.A., Irani, K.B.: An Algorithmic Solution of n-Person Games. In: Proceedings AAAI 1986, pp. 158–162 (1986)Google Scholar
  9. 9.
    Matsumoto, M., Nishimura, T.: Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudorandom Number Generator. ACM Transactions on Modeling and Computer Simulation 7(1), 3–30 (1998)CrossRefGoogle Scholar
  10. 10.
    Newborn, M.M.: The Efficiency of the Alpha-Beta Search on Trees with Branch-Dependent Terminal Node Scores. Artificial Intelligence 87(1-2), 225–293 (1977)MathSciNetGoogle Scholar
  11. 11.
    Sturtevant, N.R.: Last-Branch and Speculative Pruning Algorithms for Maxn. In: Proceedings IJCAI 2003, pp. 669–678 (2003)Google Scholar
  12. 12.
    Sturtevant, N.R., Korf, R.E.: On Pruning Techniques for Multi-Player Games. In: Proceedings of AAAI 2000, pp. 201–208 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • H. (Jeroen) H. L. M. Donkers
    • 1
  • H. Jaap van den Herik
    • 1
  • Jos W. H. M. Uiterwijk
    • 1
  1. 1.Institute for Knowledge and Agent Technology, MICCUniversiteit MaastrichtMaastrichtThe Netherlands

Personalised recommendations