Reference bot enhancement ========================= Here is another possible enhancement to the reference bot which I am currently testing. I do not yet have anything conclusive enough to report, but it looks good so far with a small number of games.
But even if this idea doesn't pan out, it will produce a much more natural playing style without weakening the bot. Here is how it works. We will use 1000 playouts for our example: 1. Modify the bot to keep a "futures" table. At the end of each playout, tally the wins for white and black for each point on the board. (I tally -1 for a white win, 1 for a black win to get a final score from -1000 to 1000 for each point.) 2. When the 1000 playouts are complete, compute an "uncertainty value" for each point, where 1.0 is completely uncertain, and 0.0 is completely certain. A point is completely certain if at the end of each playout it was ALWAYS owned by one player or the other. It's completely uncertain if it won 50% of the time for either side. 3. When determining which move to play, apply an uncertainty delta to the computed score of each move. This is simply some fraction of the "uncertainty value" and the best value I've tested so far is 0.025. So you get a bonus that ranges from 0.0 to 0.025. 4. Choose the move with the best (sc + uncertainty_delta.) 5. The incentive must be small, large incentives will destroy the playing strength. For instance 0.1 is too high and weakens it. The value that is testing the best for me (of the ones I've tried so far) is 0.025 6. This may test at some levels better than others. I'm testing at 2000 playouts. The idea is to gently encourage the bot to avoid playing to points that are clearly a forgone conclusion (or conversely, encourage it to play where the "action" is.) This should make the bot play much less artificially. Near the end of the game it will prefer moves to unresolved points. Earlier in the game it will avoid moving to areas that are "probably" already won or lost. My feeling is that these "incentives" should probably be calculated in a non-linear way, but what I described is a good starting point. From experiments in the past it seems more important to put the focus and most of the weight on avoiding play to highly certain points. So I will try some non-linear formula next. - Don
signature.asc
Description: This is a digitally signed message part
_______________________________________________ computer-go mailing list [email protected] http://www.computer-go.org/mailman/listinfo/computer-go/
