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Monte-Carlo Tree Search (MCTS) is a new best-first search guided by the results of Monte-Carlo simulations. In this article, we introduce two progressive strategies for MCTS, called progressive bias and progressive unpruning. They enable the use of relatively time-expensive heuristic knowledge without speed reduction. Progressive bias directs the search according to heuristic knowledge. Progressive unpruning first reduces the branching factor, and then increases it gradually again. Experiments assess that the two progressive strategies significantly improve the level of our Go program Mango. Moreover, we see that the combination of both strategies performs even better on larger board sizes.

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Using full-game databases and optimized tree-search algorithms, the game of Kalah is solved for several starting configurations up to 6 holes and 5 counters per hole. The main search algorithm used was iterative-deepening MTD(f). Major search enhancements were move ordering, transposition tables, futility pruning, enhanced transposition cutoff , and endgame databases.