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Monte-Carlo Alpha-Beta, (MCαβ)
is a Best-First search algorithm based on Monte-Carlo Tree Search and a shallow alpha-beta depth-first-search. It is used in Lines of Action and in conjunction with UCT (Upper Confidence bounds applied to Trees) also in Chess. It is similar to the the Best-First Minimax-Search proposed by Richard Korf and Max Chickering [1].

Four Phases

MCαβ can be divided into four strategic phases, repeated as long as there is time left:
  1. In the Selection phase the tree is traversed from the root node until it selects a leaf node that is not added to the tree yet
  2. The Expansion strategy adds the leaf node to the tree
  3. The Playout phase performs a shallow alpha-beta search
  4. The Backpropagation strategy propagates the results through the tree

See also



Publications


Forum Posts


References

  1. ^ Richard Korf, Max Chickering (1996). Best-First Minimax Search. Artificial Intelligence, Vol. 84, No 1-2
  2. ^ Re: Announcing lczero by Daniel Shawul, CCC, January 21, 2018 » LCZero

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