At the moment I (and another member of my group) are doing research on applying machine learning to constructing a static evaluator for Go positions (generally by predicting the final ownership of each point on the board and then using this to estimate a probability of winning). We are looking for someone who might be willing to help us build a decent tree search bot that can have its static evaluator easily swapped out so we can create systems that actually play over GTP. As much as we try to find quantitative measures for how well our static evaluators work, the only real test is to build them into a bot.
Also, if anyone knows of an open source simple tree search bot (perhaps alpha-beta or something almost chess like) for Go, we might be able to modify it ourselves. The expertise of my colleague and I is in machine learning, not in tree search (although if worst comes to worst I will write my own simple alpha-beta searcher). We would be eager to work together with someone on this list to try and create a competitive bot. We might at some point create a randomized evaluator that returns win or loss nondeterministically for a position instead of a deterministic score, so an ideal collaborator would also have some experience with implementing monte carlo tree search (we could replace playouts with our evaluator to some extent perhaps). But more important is traditional, chess-like searching algorithms. If anyone is interested in working with us on this, please let me know! We have a prototype static evaluator complete that is producing sane board ownership maps, but we will hopefully have many even better ones soon. - George _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
