Multi-cut αβ-pruning in game-tree search

Multi-Cut αβ-Pruning in Game-Tree Search

The efficiency of the αβ-algorithm as a minimax search procedure can be attributed to its effective pruning at so-called cut-nodes; ideally only one move is examined there to establish the minimax value. This paper explores the benefits of investing additional search effort at cut-nodes by also expanding some of the remaining moves. Our results show a strong correlation between the number of pr...

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...

Multi-processor Game-tree Search

The design, implementation, and performance of two chess programs running on a multi-processor system are presented. Methods for distributing and enhancing the alpha-beta algorithm in a parallel environment are discussed. With the Principal Variation Splitting method, a four processor chess program yields a 3.35 fold speed-up over the sequential version. [Recreated from files dated 4 May 1984 i...

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

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...