Author: Jay Scott
Date: 13:20:24 04/28/05
Go up one level in this thread
On April 27, 2005 at 17:57:10, Michael Yee wrote: >Okay, so more work could be done here. I'm considering using very large >(hopefully representative) training sets, not just self-play (as long as I can >find the computing time!). In self-play, mistakes may go uncorrected. If the program does not know how to punish a given mistake, it will make the mistake and never notice. (Backgammon programs that learn this way escape the danger because the dice rolls force them to explore different situations.) But collections of grandmaster games are also poor. Grandmaster moves are produced by reasoning that a program cannot hope to approximate (just as program moves are produced by reasoning that a grandmaster cannot hope to approximate), so the lessons that can be drawn from them are necessarily shallow. There is no such thing as a "representative" training set of human-produced moves or positions, and machine learning algorithms are easily misled. I think the problem of finding good training data is unsolved. In fact, I think that that is the central problem: We have good learning algorithms that can fit data accurately and avoid overfitting. We do not have good data. Jay
This page took 0 seconds to execute
Last modified: Thu, 15 Apr 21 08:11:13 -0700
Current Computer Chess Club Forums at Talkchess. This site by Sean Mintz.