Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search

Monte-Carlo evaluation consists in estimating a position by averaging the outcome of several random continuations, and can serve as an evaluation function at the leaves of a min-max tree. This paper presents a new framework to combine tree search with Monte-Carlo evaluation, that does not separate between a min-max phase and a MonteCarlo phase. Instead of backing-up the min-max value close to the root, and the average value at some depth, a more general backup operator is defined that progressively changes from averaging to min-max as the number of simulations grows. This approach provides a fine-grained control of the tree growth, at the level of individual simulations, and allows efficient selectivity methods. This algorithm was implemented in a 9

• Type: Conference
• Year: 2006
• Where: CG
• Authors: Rémi Coulom
• Keywords: CG 2006, Computer Graphics, Efficient Selectivity Methods, Evaluation Function, Monte-Carlo Evaluation