Hello & thank in advance for any interests/ responses. I'm unfortunately (or not) doing a dissertation as part of my final year project (undergraduate) on the game of Go. The exact title is: "Can the game of go be solved? Analysis of computational methodologies for go." And I have included my overall objectives below.
I have many works from different people on different aspects of Computer Go which would make for great inclusion at different parts - but overall I am still gravely struggling. In reviewing some of these my greatest difficulty is in understanding exactly how say Monte-Carlo-UCT or even Alpha-Beta testing (pruning, etc) occur so as to be able to give a simplified depiction (illustrated or otherwise) of the process. Can this be done without having to go through the source code of say something like GNU Go? Also another difficulty I've had is in trying to get further information on the commonly referred top ranking packages, Handtalk, Go++, Many Faces of Go, etc due to their commercial nature? (the only thing I've been able to find which is a bit outdated: http://www.inventivity.com/OpenGo/Papers/EditedGoPapers.html). Lastly can any general categorisation - distinction be made of current approach/ implementations in trying to 'solve' Go. in comparison to say traditional disciplines used in trying to solve games (complex or otherwise) via computer? To put simply I am trying to have some core root comparison in current methodologies (if there is any?). If anyone has any suggestions/ guidance on anything mentioned - I would be eternally indebted. ================================== 5.1 OBJECTIVES . To concisely review all game playing aspects of Go (rules, openings, middle game, etc) and its relevance to the complication of meaningful measurements of interest. . To evaluate, gain and develop further understanding of specific game aspects including (eg): - Representation: . Eyes . life-and-death . territory estimates and weakness - Move Evaluation: . Territorial and strategic affluence. . Address specific and current implementation methodologies including: - Search algorithms (Alpha-Beta - local/global, Monte-Carlo -UCT) - Move Generation - Positional Evaluation (Patterns, Neural Networks) . To detail inadequacies in research and reasons for shortfalls where applicable. _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
