I'm currently working on a project where I apply a monte-carlo tree search type algorithm on a card game. In the game I choose, there is no drawing of cards: all players are dealt 8 cards and play 1 card by round for 8 rounds. My problem is the following:
If I run once the shuffle of the cards at the beginning of a game, the algorithm will progressively "learn" what is hidden inside the hands of others. So after each tree search I must reshuffle the hands of every other player. But that strategy does not fit the game I chose because I may end up in situation where one player end up with cards that does not match what he played in previous rounds.
Do you see an easy way around this problem? Do you even think MCTS may solve this type of game?
Answer: use determinization, aka. before each round of selection, expansion, simulation and backpropagation, you randomize what you do not know in the game "as if you knew" everything. An implementation of MCTS for a similar game using determinization has been done here.
Thanks again to Betcha for pointing it out.