There are approaches. One is General Game Playing where the rules are given as a set of logic formulas that basically describe a state machine http://en.wikipe

There are approaches. One is General Game Playing where the rules are given as a set of logic formulas that basically describe a state machine http://en.wikipedia.org/wiki/General_game_playing http://en.wikipedia.org/wiki/General_game_playing http://www.general-game-playing.de/literature.html http://www.general-game-playing.de/literature.html GGP is then about deriving knowledge about the game and its state evaluation using a) the rules directly, b) the represented state tree or c) past matches in that very game. The other one I know of is a mechanism where you use reinforcement learning and assume one state in the beginning. With more info, you start splitting the state into several states using decision tree split criteria, such as cross entropy, and you end up obtaining a game state tree together with the knowledge to play reasonably well. Problem: I don't remember how it's called.