Risk management in game-tree pruning

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

Risk Management in Game-Tree Pruning

In the half century since minimax was first suggested as a strategy for adversary game search, various search algorithms have been developed. The standard approach has been to use improvements to the Alpha-Beta (α-β) algorithm. Some of the more powerful improvements examine continuations beyond the nominal search depth if they are of special interest, while others terminate the search early. Th...

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...

Pruning Game Tree by Rollouts

In this paper we show that the α-β algorithm and its successor MT-SSS*, as two classic minimax search algorithms, can be implemented as rollout algorithms, a generic algorithmic paradigm widely used in many domains. Specifically, we define a family of rollout algorithms, in which the rollout policy is restricted to select successor nodes only from a subset of the children list. We show that any...

A Review of Game-Tree Pruning

Chess programs have three major components: move generation, search, and evaluation. All components are important, although evaluation with its quiescence analysis is the part which makes each program’s play unique. The speed of a chess program is a function of its move generation cost, the complexity of the position under study and the brevity of its evaluation. More important, however, is the...