Hello Everyone I've been working for a while on a computer go player which takes a rather different tack[0]. Rather than using embedded programmatic domain knowledge (like GNU Go) or dynamic evaluation of board positions (UCT etc), it uses domain knowledge inferred from game records and a complex look-up during play.
My approach is to define a linearisation of the board with respect to a position (or a set of linearisations, taking into account symmetry), I then use classical string processing techniques, principally a large prefix tree. Conceptually this tree is very large (one leaf for every vertex for every possible board position), but it is not fully expanded. I'm foreseeing that crafting rules relating to the expansion of the tree to be a core problem. Does anyone know of any research into similar approaches? The program will be slow and memory hungry to train, but should be fast to play. I'm anticipating it will be strong at the opening but possibly confused by random moves (i.e playing on the edge of the board). Currently I have developed a core system which is now plays games that games that look at least a little like go. What I'm after now is a good collection of games to train it on, so I can see check whether further developments are making a positive difference. What I think I need is a relatively homogeneous collection of tens or hundreds of thousands of 19x19 games of varying levels. Does anyone know of a collection such as this I can download relatively simply? cheers stuart [0] http://code.google.com/p/jgogears/ _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
