Hi, long-time lurker and occasional poster here, Thank you for the paper. I hope you don't mind me asking a few very basic questions, since I am having trouble understanding exactly what you are doing.
Let's say we are using a linear classifier. Then our output (the predicted move) should look like: argmax_i (*y*[i]), where *y*[i] = *w1*[i] · m1 + *w2*[i] · m2 + b Where each w[i] is a weight vector for location i on the board, the m's are the (column) input vectors (which I assume are 1 at the move location and zero elsewhere), and b is the bias term. To train our classifier online, we want to do something like: (1) Generate a prediction for a training example. (2) Calculate the error. (3) Update the feature weights. (4) Repeat. If I understand, online training happens during the course of one game, as we are playing. Moreover, we are using our classifier to generate moves to select in the first phase of our simulation, as a replacement for MCTS, and before playouts. Now this is where I have to start guessing the details. Are our training examples playouts, and is our error function just 0 if the playout wins, and 1 if it loses? And as we run more playouts, the classifier will update its weights and select a different sequence of moves in the first phase of our simulation (analogous to selecting different paths down the search tree based on node scores in MCTS)? And when we use up our allotted time for one turn we just return the next move (from the current position) that our classifier predicts, based on its current weights? The paper says we fix the number of moves we select with the classifier before running playouts (unlike starting from the root and expanding in MCTS). This is where things start getting really fuzzy for me. Do we propagate the results of a playout back up this sequence? i.e. if we get a win, do we perform updates of our classifier for each two-move sequence in the full sequence? I would really like to get to the deeper questions about interpreting what is really going on, but I first need to make sure I am on the right page here. Sincere apologies for the stupid questions. I really hope my understanding didn't get derailed so early on that most of my questions in this message are gibberish. But I did want to show that I actually made a concerted effort to understand the paper before asking what on earth it is all about! On Tue, Jun 28, 2011 at 7:30 PM, Peter Drake <[email protected]> wrote: > It doesn't beat RAVE, but it's an interesting result. Our paper will appear > at the International Conference on Artificial Intelligence (ICAI) in Las > Vegas: > > https://webdisk.lclark.edu/**drake/publications/sylvester-**icai-2011.pdf<https://webdisk.lclark.edu/drake/publications/sylvester-icai-2011.pdf> > > Peter Drake > http://www.lclark.edu/~drake/ > > > > ______________________________**_________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/**mailman/listinfo/computer-go<http://dvandva.org/cgi-bin/mailman/listinfo/computer-go> >
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