Author: Michael Neish
Date: 05:53:48 01/20/06
Hi, I just tried a little experiment with my as yet simple program. I added a mobility function for the Queen as the only way of scoring this piece, i.e., no piece-square tables. When I ran the program, I found that it increased the required number of nodes to reach the same depth in a given position, by roughly 50% to ply 11, which I found curious. Thinking about it, adding this new feature could be resulting in a greater number of positions that score roughly equally, hence search needs to sift through more nodes to find the best one. The opposite is true: when there's a really obvious best move like moving one's Queen away from a Pawn that's attacking it, search tends to go deeper faster since it can quickly eliminate bad branches. Have I got the correct interpretation? If so, would adding more refinements to Eval() result in an even greater number of nodes required to the same depth, or not necessarily? If it would, what can one do to prevent it? Could one, say, design Eval() so that position scores don't cluster around the zero mark, but are instead more spread out so that search doesn't get bogged down trying to distinguish between positions that are just a point different? Easier said than done, maybe. Properly done, though, I wonder if it might be a viable strategy for reducing the size of the tree. Any comments? Cheers, Mike.
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