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DeepButi

Learning methods in trick-taking card games

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I'm looking for learning methods aplied to card trick-taking team games (bridge-like). As so, it's: a) NOT a two-player game b) NOT a complete information games -> minimax, alpha/beta prunning does not fit well. Adding Montecarlo would help a little, but that's not my problem just now. c) NOT a war-like, strategic-like game and most links don't apply. I cannot see how Genetic/neural theory apllies in such kind of games because I'm stopped on how to evaluate "moves", strategy, ... Actually my program (for a local card game) wins 46% games against humans (well, one human as ally and two human opponents), it plays ... mmm ... moderate, and I would like it to learn from history. Any suggestions? Thanks a lot

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Guest Anonymous Poster
One thing you may wish to investigate is Temporal Difference Learning.

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Guest Anonymous Poster
You may want to check out POKI, a poker playing agent that must deal with many of the challenges you are facing. http://www.cs.ualberta.ca/~games/poker/

The papers are your best bet for pertinant information but are very verbose. This is an active and challanging area of research.

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