Monte Carlo Tree Search or other algorithms for a stochastic card game?

Question

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 is 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?

Answer 1

MCTS will be better. Despite which one do you choose you will have to deal with incomplete information which is the central problem here.

The Monte-Carlo Tree Search algorithm (MCTS) has been recently proposed for decision-making problems. Applications are numerous and varied and contain notably games. In games, MCTS outperforms alpha-beta techniques when

evaluation functions are hard to design. The most known implementation of MCTS is Upper Confidence Bound (UCT), which is presented below. 

You can refer to the following link for more information: https://hal.inria.fr/hal-01406496/document