I’m trying to develop an AI for a card game and I’m a bit stuck about the technique/algorithm I should use. Here are a few assumptions about the game:
- After the cards are distributed to players, there is no randomness. I mean here that every player can choose which cards he plays but no random process occurs as when distributing the cards at the beginning of the game.
- There is restriction about the cards which can be played when a card was already played.
- The player which wins the trick, plays then first. E.g. Player 1 plays a card, Player 2 plays a card and wins. Then Player 2 plays a card and then Player 1 plays.
I know a lot of hints/rules (e.g. if I know the player has cards A, B, C then I should play D) which helps me to win to the game. Thus I first wanted to use a Bayesian network to describes those rules. The problem is that I don’t know any probabilities to assign, but I could compute an heuristic using the history of played games (against a human). Second problems, it is very likely that I don’t know all rules and that there is some implicit rules which are needed by the AI to find the optimal play.
I’m unsure if this would be a good way to develop an AI for such a card game?
I am also wondering if there is others techniques which would best fit to the problem. For instance, I had a look at minimax (maybe with a pruning algorithm), but would be a good option for this problem? I’m quite unsure since the most important plays are at the beginning of the game when there is the highest unknown parameters (most of cards are not played yet).