Best node search

This article may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts, without removing the technical details. (October 2016) (Learn how and when to remove this template message)

Best node search (BNS), originally known as fuzzified game tree search, is a minimax search algorithm, developed in 2011. The idea is that the knowledge that one subtree is relatively better than some (or all) other(s) may be propagated sooner than the absolute value of minimax for that subtree. Then a repetitive search narrows until a particular node is shown to be relatively best.

First an initial guess at the minimax value must be made, possibly based on statistical information obtained elsewhere. Then BNS calls search that tells whether the minimax of the subtree is smaller or bigger than the guess. It changes the guessed value until alpha and beta are close enough or only one subtree allows a minimax value greater than the current guess. These results are analogous, respectively, to "prove best" and "disprove rest" heuristic search strategies.

The search result is the node (move) whose subtree contains the minimax value, and a bound on that value, but not the minimax value itself.^[1] Experiments with random trees show it to be the most efficient minimax algorithm.^{[citation needed]}

Pseudocode[edit]

function nextGuess(α, β, subtreeCount) is     return α + (β − α) × (subtreeCount − 1) / subtreeCount   function bns(node, α, β) is     subtreeCount := number of children of node      do         test := nextGuess(α, β, subtreeCount)         betterCount := 0         for each child of node do             bestVal := −alphabeta(child, −test, −(test − 1))             if bestVal ≥ test then                 betterCount := betterCount + 1                 bestNode := child         (update number of sub-trees that exceeds separation test value)         (update alpha-beta range)     while not (β − α < 2 or betterCount = 1)      return bestNode 

The default nextGuess function above may be replaced with one which uses statistical information for improved performance.

Generalization[edit]

Tree searching with Murphy Sampling^[2] is an extension of Best Node Search to non-deterministic setting.

External links[edit]

• fuzzy minimax algorithms

References[edit]