Use this search procedure to study or select actions in a defined game tree when legal moves, terminal values, depth, and evaluation rules are available.

Candidates

Required object input.

Parent visits

Required integer input.

Exploration constant

Required number input.

Alpha bound

Required number input.

Beta bound

Required number input.

Rank Monte Carlo tree-search actions with UCT scores constrained by alpha and beta bounds. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

UCT ranking

The resulting uct ranking returned as an object.

• Search quality depends on the game model, evaluation function, horizon, move ordering, branching factor, stochastic assumptions, and computational budget. A returned move is optimal only within the searched model and bounds.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Compare search depths and ordering strategies, verify small trees exhaustively, and test decisions across representative positions rather than relying on one example.

• Alpha-Beta Pruning Calculator

  Evaluate a finite minimax tree while pruning branches that cannot affect the result.

• AlphaZero PUCT Calculator

  Rank candidate actions with the prior-guided upper confidence formula used by AlphaZero-style search.

• Whittaker Beta Diversity

  Compute Whittaker beta diversity betaW = gamma / mean alpha richness from two site species counts and their shared count.