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I am currently working on an implementation of a 2 player trick-and-draw card game, similar to 66 or Schnapsen. Basically you need to gather points by winning tricks and while there are cards in the pack, both players draw a card after each round.

I am at the point of programming a good AI for the game that does not cheat, but really calculates the best moves by using only the information it has at the given game state. I am stuck deciding which algorithm or logic would be the best to use. I decided against algorithms like Alpha-Beta pruning because there are too much hidden information especially at the beginning of the game. I read many interesting things about the Monte Carlo Tree Search and the related UCT search, but because the game has stochastic elements, the tree needed to be searched would grow huge in a short time.

Which algorithm or approach would be the best to use?

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2 回答 2

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这是 UCT 在 Klondike Solitaire 中的应用的链接。MCTS 非常适合该问题，因为它可以很好地处理随机性。

您可以查看论文中描述的稀疏方法，以了解限制树宽度的方法。

于 2012-06-14T14:39:40.180 回答
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MCTS 肯定会更好。无论您选择哪一个，您都必须处理不完整的信息，这是这里的核心问题。

于 2012-06-14T13:03:13.200 回答