Probability-Based Game Tree Pruning

A New Pruning Method for Solving Decision Trees and Game Trees

The main goal of this paper is to describe a new pruning method for solving decision trees and game trees. The pruning method for decision trees suggests a slight variant of decision trees that we call scenario trees. In scenario trees, we do not need a conditional probability for each edge emanating from a chance node. Instead, we require a joint probability for each path from the root node to...

Probability of Potential Model Pruning in Monte-Carlo Go

In this study, we tackled the reduction of computational complexity by pruning the igo game tree using the potential model based on the knowledge expression of igo. The potential model considers go stones as potentials. Specific potential distributions on the go board result from each arrangement of the stones on the go board. Pruning using the potential model categorizes the legal moves into e...

An Analysis of Forward Pruning

Several early game-playing computer programs used forward pruning (i.e., the practice of deliberately ignoring nodes that are believed unlikely to affect a game tree’s minimax value), but this technique did not seem to result in good decision-making. The poor performance of forward pruning presents a major puzzle for AI research on game playing, because some version of forward pruning seems to ...

Risk Management in Game-tree Pruning Risk Management in Game-tree Pruning

Confidence Bound Algorithms in Game Trees Effective Monte Carlo Tree Search through Bernstein’s Inequality and the Law of the Iterated Logarithm

Game trees have been utilized as a formal representation of adversarial planning scenarios such as two-player zero-sum games like chess [1, 2]. When using stochastic leaf values based on Bernoulli trials to model noisy game trees, a challenging task is to solve the Monte Carlo Tree Search (MCTS) problem of identifying a best move under uncertainty. Confidence bound algorithms are investigated a...