Author: Mathieu Pagé
Date: 16:12:44 07/14/05
Go up one level in this thread
On July 14, 2005 at 16:03:19, Reinhard Scharnagl wrote: >On July 14, 2005 at 15:29:31, Mathieu Pagé wrote: > >>On July 14, 2005 at 15:14:35, Reinhard Scharnagl wrote: >> >>>Hi Mathieu, >>> >>>I have made some thoughts on it. It tends to say good bye to negamax approaches. >>>Because the same position will be evaluated differently from personalized points >>>of view. > >>I do not see why we could not use a negamax approach with a opponent based >>learning. >> >>The learning i'm talking about is book learning and weight learning. Thoses two >>techniques have already been used with succes in conjuction with negamax. >> >>Maybe you are thinking of some more advandced modeling technique. If it is the >>case i'd appreciate if you share them with us. > >Hi Mathieu, > >it will sound to hard, but such an approch is contradicting and will fail. > >I see the problem from a very different point of view. Chess is regarded to be >a zero-sum-game. But this is only true, having full information at hand. >Inventing detail evaluation functions supporting an engine with values distinct >from +1, 0, -1 is already proving, that chess could obviously not be handled as >a zero-sum-game. But the negamax approach is only working using that assumption. > >Having different evaluation models for engine personalities will mutate engines >from evaluation models into prediction models, which might be more effective, >but establishes the need for navigating through trees with pairs of evaluations. > >Reinhard. Hi Reinhard, Obviously we missunderstand each other. While playing, an engine using my idea of opponent-modeling will do the same thing as if it was a normal engine. The difference is that it will use a different book and a different evaluation function. I can not see why it could not work. Mathieu Pagé
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.