Author: Jay Scott
Date: 10:17:35 06/22/99
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On June 22, 1999 at 03:45:51, Dave Gomboc wrote: >Keep track of the computed score for a position separately from the backed-up >score for a position. If they differ significantly, an important alternative >was overlooked. You're suggesting the obvious algorithm, almost exactly what I suggested some months ago. This bit is not quite right. The scores might differ solely because of the effective search horizon. For example, after 1. e4 e5 2. f4, suppose a normal search evaluates 2... exf4 as +100 for black, or close. Backing up deep values from the book might give the move an evaluation around 0--and yet still identify 2... exf4 as the best move. There's a big score difference, but it doesn't have anything to do with overlooking an alternative. The most powerful way seems to be this: 0. Having backed up all scores below a node, 1. load these scores into the hash table, marking them as exact values so that the current search won't try to look beyond them. (They're based on a deeper search, so this search won't be able to see anything new.) 2. Search from the node. 3. If the best move is not already in the book, the program has found a novelty. This must be corrected immediately: add it to the book. A more intensive novelty-search would find all moves within, say, half a pawn of the best move. Just use the "Next Best" feature until you've got 'em all. For the Kasparov effect, repeat the novelty search on the novelties until you have a whole subtree of analysis ready for your next opponent. The new scores float up the tree and may change the best move at the original node--the ideal is to discover that the novelty that seemed at first to be second-best is actually quite strong. Offline book learning is *way* more powerful than the online book learning that chess programs use now. Here's a paper about a simple method (used in an othello program): "Toward opening book learning" by Michael Buro compressed postscript, five pages http://www.neci.nj.nec.com/homepages/mic/ps/book.ps.gz Jay
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