Best-First Minimax Search

Home * Search * Best-first minimax search

Best-first minimax search, (BFMMS) is a Best-First search algorithm based on Best-First and a shallow alpha-beta depth-first-search proposed by Richard Korf and Max Chickering.

BFMMS, MCαβ, the Rollout Paradigm and further MCTS-minimax hybrids share the same idea, to combine Best-First with Depth-First searches.

=Four Phases= BFMMS can be divided into four strategic phases, repeated as long as there is time left:
 * 1) In the Selection phase the best node is selected from the game tree via node score from the root node until it selects an unexpanded node
 * 2) The Expansion strategy adds the unexpanded child nodes to the tree
 * 3) The Playout phase performs a shallow alpha-beta search to get a node score
 * 4) The Backpropagation strategy propagates the score through the tree

=See also=
 * MCαβ
 * Rollout Paradigm

=Publications=
 * Richard Korf, Max Chickering (1996). Best-First Minimax Search. Artificial Intelligence, Vol. 84, No 1-2
 * Yaron Shoham, Sivan Toledo (2001). Parallel Randomized Best-First Minimax Search

=Forum Posts=
 * MCTS without random playout? by Antonio Torrecillas, CCC, January 01, 2012 » B*
 * Help with Best-First Select-Formula by Srdja Matovic, CCC, July 23, 2012
 * Re: Announcing lczero by Daniel Shawul, CCC, January 21, 2018 » Leela Chess Zero
 * Alpha-Beta as a rollouts algorithm by Daniel Shawul, CCC, January 25, 2018 » Alpha-Beta, Monte-Carlo Tree Search, Scorpio

=External Links=

=References=