Searching informed game trees

Extended abstract
  • Wim Pijls
  • Arie de Bruin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 650)


Well-known algorithms for the evaluation of the minimax function in game trees are alpha-beta [Kn] and SSS* [S t]. An improved version of SSS* is SSS-2 [Pij1]. All these algorithms don't use any heuristic information on the game tree. In this paper the use of heuristic information is introduced into the alpha-beta and the SSS-2 algorithm. The subset of nodes which is visited during execution of each algorithm is characterised completely.


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    G. M. Baudet, On the branching factor of the alpha-beta pruning algorithm. Artificial Intelligence 10 (1978), pp 173–199.Google Scholar
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    T. Ibaraki, Generalization of Alpha-Beta and SSS* Search Problems, Artificial Intelligence 29 (1986), pp 73–117.MathSciNetGoogle Scholar
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    D.E.Knuth and R.W.Moore, An Analysis of Alpha-Beta Pruning, Artificial Intelligence 6 (1975), pp 293–326.CrossRefGoogle Scholar
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    W. Pijls and A. de Bruin, Another view on the SSS * algorithm, in: Algorithms, Proceedings International Symposium SIGAL '90, Tokyo, August 1990.Google Scholar
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    W. Pijls and A. de Bruin, Searching informed game trees, Report EUR-CS-92-02, Department Computer Science, Erasmus University Rotterdam.Google Scholar
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    G.C. Stockman, A Minimax Algorithm Better than Alpha-Beta?, Artificial Intelligence 12 (1979), pp 179–196.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Wim Pijls
    • 1
  • Arie de Bruin
    • 1
  1. 1.Erasmus University RotterdamThe Netherlands

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