Author: Dave Gomboc
Date: 07:53:23 07/07/03
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I and my supervisors have had a paper accepted for ACG 10 on this topic. It is not a silver bullet, though. It isn't available yet, there's still some stuff to do on it. Dave On July 07, 2003 at 01:14:43, Peter McKenzie wrote: >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
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