Quick question: When using this mailing list, how to I explicately reply to a thread, so far I've just been editing the subject and sending it to computer-go@computer-go.org.
Regarding use in a MTCS engine, I strongly suspect it would perform poorly in its current form. It is quite poor at life and death, especially if you give it situations very different from the training set. One issue with the method of training was I only used games which were played until the end (i.e. didn't end in resignation), as a result the model is extremely biased that large groups of stones live simply because games not ending in resignation tend to be close and not have large groups die. Depending on how hard it would be to integrate into a MCTS I could try it. My hope was that a well trained evaluator could allow for alpha beta pruning to be competitive with MCTS, interested to hear the groups thoughts on this. Regarding 9x9, I believe Alvaro Begue has explored this idea in a way which perhaps would work better in a go engine. He used pachi to generate a database of games by playing against itself and then trained a model in a similar fashion to what I did. I'm not sure about the results of his experiments. If someone can point me to a large database of 9x9 games it would be easy to edit my code to do that. -Justin
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