0 votes
1 view
in AI and Deep Learning by (27k points)

I want to develop a two-player game with imperfect information - "Stratego".

The game is "somewhat" like chess but initially, we don't know anything about the ranks of the opponent's pieces. When a piece attacks or is attacked by some opponent's piece, their ranks are revealed and the higher rank piece kills/captures the lower rank piece. More detail on the game can be found here.

I did a little research. I read "Opponent Modeling in Stratego" by J.A. Stankiewicz. But I couldn't find a complete tutorial on how to develop the game. I have successfully developed before a two-player game - "Othello" a.k.a. Reversi, and I'm familiar with MINIMAX algorithm and alpha-beta pruning.

I found somewhere that Monte-Carlo Tree Search is also used in developing zero-sum two-player games. Can it be used for games like Stratego? Can I get a complete tutorial for the same?

Any other tutorial not involving Monte-Carlo Tree Search would also be useful :)

1 Answer

0 votes
by (57.2k points)

It seems MCTS can be used for imperfect information games if it's modified to ISMCTS. Unfortunately, there is a dearth of examples and I am having a similar problem in that a particular game am developing involves both chance and incomplete information. But you can prevail etheses.whiterose.ac.uk/8117/1/Feb 16 - FINAL.pdf 

I think MCTS would have a difficult time in Stratego since the initial spreading function is so large while the best play is very dependent on the ground-truth of the game. MCTS in the best case, give you a play that's statistically good amongst all the possible variations of your opponent's pieces, but the best next move is highly dependent on which particular variation they've chosen.

Welcome to Intellipaat Community. Get your technical queries answered by top developers !


Categories

...