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
Question
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
No correct solution
OTHER TIPS
There are dozens of "alternatives", or rather - modifications, with at least two basic models:
Use a genetic algorithm/neural network approach, where competing versions of the AI play each other and the better ones survive.