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Rediscovering *-Minimax Search

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 3846)

Abstract

The games research community has devoted little effort to investigating search techniques for stochastic domains. The predominant method used in these domains is based on statistical sampling. When full search is required, Expectimax is often the algorithm of choice. However, Expectimax is a full-width search algorithm. A class of algorithms were developed by Bruce Ballard to improve on Expectimax’s runtime. They allow for cutoffs in trees with chance nodes similar to how Alpha-beta allows for cutoffs in Minimax trees. These algorithms were published in 1983—and then apparently forgotten. This paper “rediscovers” Ballard’s *-Minimax algorithms (Star1 and Star2).

Keywords

  • Leaf Node
  • Search Window
  • Search Phase
  • Game Tree
  • Chance Event

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.

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References

  1. Ballard, B.: The *-Minimax search procedure for trees containing chance nodes. Artificial Intelligence 21(3), 327–350 (1983)

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© 2006 Springer-Verlag Berlin Heidelberg

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Hauk, T., Buro, M., Schaeffer, J. (2006). Rediscovering *-Minimax Search. In: van den Herik, H.J., Björnsson, Y., Netanyahu, N.S. (eds) Computers and Games. CG 2004. Lecture Notes in Computer Science, vol 3846. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11674399_3

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  • DOI: https://doi.org/10.1007/11674399_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32488-1

  • Online ISBN: 978-3-540-32489-8

  • eBook Packages: Computer ScienceComputer Science (R0)