Bayeisan/Probablistic Playouts in Computer Go
Alexander Terenin <aterenin <at> ucsc.edu>
2014-09-25 22:28:10 GMT
I’m a PhD student in statistics at the University of California, Santa Cruz who previously worked on the
Go program Orego, currently in the process of applying for the NSF fellowship. I am working on a Bayesian
statistics - related research proposal that I would like to use in my application, and wanted to know if
someone was aware of any research related to my topic that has been done.
Currently, it seems most MCTS-based Go programs, in the playouts, treat the strength (win rate) of each
move as a fixed, unknown value, which is then estimated using frequentist techniques (specifically, by
playing a random game, and taking the estimate to be wins / total runs). Has anyone attempted to instead
statistically estimate the strength of each move using Bayesian techniques, by defining a set of prior
beliefs about the strength of a certain move, playing a random game, and then integrating the information
gained from the random game together with the prior beliefs using Bayes' Rule? Equivalently, has anyone
defined the strength of each move to be a random variable rather than a fixed and unknown value? Without
making this email too long, there’s some theoretical advantages that might allow for more information
to be extracted from each playout if this setup is used.
If you are aware of any work in this direction that has been done, I would love to hear from you! I’ve been
looking through a variety of papers, and have yet to find anything - it seems that any work remotely related
to Bayes’ Rule has concerned the tree, not the playouts.
Thank you in advance,
aterenin <at> ucsc.edu
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