Risk Management in Game-Tree Pruning

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

Properties of Forward Pruning in Game-Tree Search

Forward pruning, or selectively searching a subset of moves, is now commonly used in game-playing programs to reduce the number of nodes searched with manageable risk. Forward pruning techniques should consider how pruning errors in a game-tree search propagate to the root to minimize the risk of making errors. In this paper, we explore forward pruning using theoretical analyses and Monte Carlo...

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-Based Game Tree Pruning

In game-tree search, a point value is customarily used to measure position evaluation. If the uncertainty about the value is to be reflected in the evaluation, which is described with probabilistic distribution or probabilities, the search process must back up distributions from leaf nodes to the root. It is shown that even though the merit value of a node is described by probabilities, α-β bou...

Last-Branch and Speculative Pruning Algorithms for Max"

Previous work in pruning algorithms for max" multi-player game trees has produced shallow pruning and alpha-beta branch-and-bound pruning. The effectiveness of these algorithms is dependant as much on the range of terminal values found in the game tree as on the ordering of nodes. We introduce last-branch and speculative pruning techniques which can prune any constantsum multi-player game tree....