Hi Rémi and all, It's not final version of his thesis, rather it has some (or a lot of :) errors. Please wait for the final version.
-Hideki Rémi Coulom: <[EMAIL PROTECTED]>: >Hi, > >I found the Master Thesis of Nobuo Araki is available online: >http://ark.qp.land.to/main.pdf > >Abstract: >Recently in the Go program, there was a breakthrough by the Monte-Carlo >method using >a game tree search method called UCT (UCB applied to trees, UCB stands >for Upper Confidence >Bounds) in combination with the reduction of search space by move >prediction. By >this method, Go programs easily become stronger than existing programs. >However, there >are hardly any studies concerning the relationship between the strength >of a program, and >the accuracy of move prediction, which is integrated into the >Monte-Carlo method; therefore, >we cannot assume the direction of future research that makes stronger >programs. In this >study, we developed a move prediction system based on machine learning >techniques, and >researched the relationship between the accuracy of move prediction, and >the strength of >Monte-Carlo method. Our move prediction system based on the maximum >entropy method >attained top level accuracies of those days. Furthermore, it became >clear that even when >the move prediction accuracy goes higher, the programs do not always >become stronger. We >investigated the reasons behind this result. Additionally, we have >attempted to create a Go >player by enforcing move prediction, but the result was not beyond >satisfactory. We will also >describe the reasons behind this result. > >Rémi >_______________________________________________ >computer-go mailing list >computer-go@computer-go.org >http://www.computer-go.org/mailman/listinfo/computer-go/ -- [EMAIL PROTECTED] (Kato) _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
