Author: Peter McKenzie
Date: 22:14:43 07/06/03
I'm interested in trying some automated evaluation tuning, is anyone else doing this at the moment? Interested in hearing about any successes or failures in this area. TD learning looks like the most obvious thing to start thinking about, the following paper is a good introduction: http://cs.anu.edu.au/~Lex.Weaver/pub_sem/publications/ICCA-98_equiv.pdf Also, here is Dan Homan's pseudo code from a few years back: http://fortuna.iasi.rdsnet.ro/ccc/ccc.php?art_id=117970 I'm not 100% convinced by TD learning, but it certainly looks interesting. As I understand it TD learning basically uses the scores from the next few positions to give a (hopefully) better estimate of the score for the current position. It then adjusts the eval weights so that the eval (or in the case of TDLeaf, the eval of the position at the tip of the PV) moves towards the estimate. OK, technically it uses all the remaining positions in the game for its score estimate, but in practice this is heavily weighted towards the next few positions. It's a pretty cool idea really. One problem I see is that different features will be tuned at different rates. Common features will of course be tuned quite quickly while rare features that occur only occasionally will be tuned slowly. This is to some extent unavoidable but maybe it makes sense to slow the rate of change for weights of common features before doing the same with rare features. Possibly a minor point though. Peter
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.