Author: Uri Blass
Date: 12:37:59 02/06/03
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On February 06, 2003 at 14:35:18, Tom Likens wrote: >On February 06, 2003 at 13:35:39, Matthias Gemuh wrote: > >> >> >>What makes a program tactically strong ? >> >>- right extensions ? >>- search depth ? >>- strange eval and eval weights ? >>- sound pruning ? >>- nps speed ? >>- or simply luck and chance ? >> >>/Matthias. > >Hello Matthias, > >I found an interesting, and not very obvious (at least to me), >result as I have continued to work on my chess program. >It has become tactically stronger as I've added more purely >positional knowledge. > >I believe that the better evaluation function is shaping the >tree (mainly, via the beta cutoff at the beginning of the >quiescence search) so that the program is wasting less time >going down unproductive branches. Because of this it is >searching deeper and is more tactically aware. > >You'll find that as you add code to recognize various patterns >that can occur (such as rook pawn and wrong color bishop >endings) that the program will become better both positionally >and tactically. > >regards, >--tom It is dependent on the program and the knowledge that you add. If you add knowledge and your program search x/10 nodes per second instead of x nodes per second then the fact that you need less nodes to get solutions to tactical problems does not mean that you are stronger in tactics. I also know examples when adding knowedge did the program weaker in tactics for other reasons. Rebel with knowledge=500 without lazy evaluation is weaker in tactics than Rebel with lazy valuation and Rebel with knowledge=500 not only search slower but also needs more nodes to get the same depth. In the case of Movei I found that the biggest improvement in tactics was not a result of adding knowledge in the evaluation but of improving the search rules. Uri
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