Author: Rémi Coulom
Date: 09:17:42 04/27/05
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On April 27, 2005 at 10:35:28, Michael Yee wrote: >Hi, > >I might have a chance to do some computer chess related research as part of my >thesis for school. (Already planned are a couple topics related to "inferring >human decision-making processes".) Computer chess will fit (in a loosely related >way) if I look at techniques for learning parameters/strategies from a database >of human GM games. > >Before I continue my literature search, etc., I just wondered what people with >experience in this area think of the following ideas: > >- learn rules/functions for when to extend or reduce search along a given line You can take a look at this: http://www.cs.ualberta.ca/~yngvi/Papers/isj03.pdf >- learn when it's safe to prune a given line (related to previous idea) Probcut and multicut are based on ideas close to machine learning, if I remember correctly. According to what I have read, Fabien Letouzey has also implemented an original "history-pruning" scheme that seems efficient, but I do not remember the details. Also, I believe it might be a good idea to use machine-learning techniques to learn extended-futility-pruning heuristics. >- learn parameters for static (leaf node) evaluation function (although a lot >has already been done here, I think) Yes, this has already been done. For chess, check Baxter & Tridgell, and Beal & Smith. The results were not impressive. >- learn/construct/discover features for a static evaluation function (some >not-so successful work may have been done here with neural networks?) I believe that finding good features necessarily requires the intervention of a human expert. But it might be a good idea to try to find ways to help a human expert find out what kind of knowledge is missing most in the evaluation function. I have no idea how, though. >- learn rules for move ordering (i.e., that try to search best moves from a >given node first to achieve more efficient cut-offs) The history heuristic and its variations are in the spirit of this. > >Specifically, I'm curious which areas are already "mature", which seem >promising/new, or even if you have any other ideas/references. Computer chess as a whole is extremely mature, so it is hard to make significant contributions. During the Ramat-Gan World Championship, I asked Amir Ban and Shay Bushinsky wheter they were using machine learning techniques in Junior. They answered something like "yes, but not the machine-learning techniques you know". That might be an interesting indication that there is potential for investigation in this direction. > >Thanks! > >Michael Good luck with your research, Rémi
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