Author: Michael Yee
Date: 07:35:28 04/27/05
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 - learn when it's safe to prune a given line (related to previous idea) - learn parameters for static (leaf node) evaluation function (although a lot has already been done here, I think) - learn/construct/discover features for a static evaluation function (some not-so successful work may have been done here with neural networks?) - learn rules for move ordering (i.e., that try to search best moves from a given node first to achieve more efficient cut-offs) Specifically, I'm curious which areas are already "mature", which seem promising/new, or even if you have any other ideas/references. Thanks! Michael
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