If the only tool you have...

"If the only tool you have is a hammer, you will see every problem as a nail." - Abraham Maslow

This week, I've been experimenting with Monte Carlo methods for a Pentago AI player. I've written pattern-based AI players for Pentago before and (somewhere) I've got an unfinished implementation that uses Minimax as well.

I didn't really expect much from this little experimental player (it's only about 100 lines of Python code) but it has turned out to be "not too bad". Despite the fact that it only considers one game state at a time... maybe that says more about my other players though ;-)

I'll try to add MCTS (Monte Carlo Tree Search) during this week, maybe even UCT (Upper Confidence bounds applied to Trees) which ought to make the player quite a bit stronger.

Computer Go reference bots

Interested in Computer Go? Then you probably already know about Don Dailey's excellent work on providing a number of Monte Carlo reference implementations. So far, he has managed to implement it in two three different languages (Java, C and some language called Vala).

The announcement is here and the Java bot is available here. It's not the prettiest Java code out there, but Don is a C programmer (I think) so he is excused. It was very interesting and educating to study the code. I've been reading a lot of the Monte Carlo Computer Go papers but I've always felt as if I've missed something fundamental since my understanding of how it works felt too... simple.

Now that I've seen the code I can say, with confidence, that it *is* quite simple. Implementing a Monte Carlo method, that is. Getting a decent (let alone, good) AI-player for 19x19 Go seems quite far from simple.