Alternative to minimax for boardgame AI? [closed]

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I'm making an AI for a zero-sum board game. It's the following game http://en.wikipedia.org/wiki/Y_%28game%29

The board i'm using is 15 fields per side, so that is 120 hexagons total. this is obviously way to big for a standard minimax approach. I was thinking I could cut off a lot because of symmetry but I still think it wont be enough.

Are there any viable alternatives for minimax when the game is too complex to search all options?

Thanks

Answer 1

There are dozens of "alternatives", or rather - modifications, with at least two basic models:

• Alpha-beta - basic pruning technique for minimax
• UCT - probabilistic method with guaranteed convergence to the minimax tree

Answer 2

Use a genetic algorithm/neural network approach, where competing versions of the AI play each other and the better ones survive.