[Computer-go] Monte-Carlo Tree Search in other games

Kahn Jonas jonas.kahn at math.u-psud.fr
Mon Nov 1 07:18:55 PDT 2010

> I agree that the number of wins is ok - but it restricts your
> implementation to games with wins and losses (I don't know
> the game considered by Petr). For Go it excludes only minor cases (jigo,
> go with loops...).

Well, the same logic applies for using the first term in the formula for
choosing which nodes to be simulated. If we write it as maximising
value_of_node + uncertainty_of_node
we select the node with highest value in the end.

For UCB1:
value_of_nodes = number of wins
uncertainty_of_node = 2 \sqrt{n}

With draws, this was suggested:
value_of_nodes = number of wins + (number of draws)/2

Of course, it is necessary for the idea to work that value_of_nodes
cannot decrease when adding simulations.

> The biggest number of simulations is ok I think for all full information
> zero-sum turn-based games (with simultaneous
> actions it's not a good idea anymore to use the most simulated action,
> nor the move with most wins).

Do you have an example of such games, and how to use MCTS for it? I have
trouble picturing it.


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