Hi All I've been lurking on the list for a while now and I have a question I'd like to ask the list, but I'd first like to take this opportunity to announce myself and my computer Go program, Oakfoam.
I am an Engineering student at the University of Stellenbosch, South Africa. I have been developing my computer Go program for the past 6 months or so mostly for my own enjoyment, however I plan on doing my final year project based on it next year. Some details about Oakfoam: - UCT algo (surprise, surprise ;)) - RAVE - Mogo 3x3 patterns - Open Source under the BSD license - Almost everything is adjustable at runtime using parameters - Achieved a 1700 ELO rating on CGOS 9x9 recently - Repo at http://bitbucket.org/francoisvn/oakfoam/ I have been working almost exclusively on 9x9. I would also like to mention that most of my parameters have not been tuned, so when I get around to that I should get some more "free" strength. However, my program mostly seems to be comparable to others when using UCT+RAVE so I'm satisfied for now. For reference I need about 100k playouts with RAVE to get 50% winrate against GnuGo 3.8 L10. Does this seem in order? I have recently been working on ELO Features like in Rémi Coloum's paper, "Computing Elo Ratings of Move Patterns in the Game of Go." I have, using the MM tool, trained some features and they seem to correspond more-or-less with the gammas in the paper (one noticeable exception is my self-atari gamma is much closer to 1). I also did a test on another collection of games and plotted a cumulative distribution comparing the ordered list of moves by gammas to the move played like in the paper. Some points on my graph: top 1: 27%, top 5: 58%, top 10: 68%. These are slightly weaker than the paper's results, however, I only used 3x3 patterns so this is to be expected. At this stage everything seemed to be in order. The next step is obviously to apply these to the playouts. I am currently testing my program with the ELO features in the playouts, but unfortunately the preliminary results don't look good. The paper speaks of an increase from a 38.2% to a 68.2% winrate, which is obviously quite substantial. However my program seems to be weaker when using the features. Results are still rolling in, so technically things could improve, but a large improvement has basically been ruled out already. So my questions are: Does anyone know where I might have gone wrong? Is there a way for me to better verify that my feature gammas are ok? Sorry for the long email, but I had a lot to say :) Any help is appreciated. -- Francois van Niekerk Email: [email protected] | Twitter: @francoisvn Cell: +2784 0350 214 | Website: http://leafcloud.com _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
