Author: Komputer Korner
Date: 01:38:19 05/23/98
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Well Don, if your experiment had nothing to do with search depths, then why not do a 1 ply search? As soon as you go above 1 ply, you skew the results because of searching. Obviously a knight move is different from the others and temporal differences are affected differently with a knight because it is not a sliding piece. If the whole article wasn't about piece values but of something else, then ignore what I have said up to now. Learning methods are affected by the environment and you conducted an environment that had a search of 4 plies as a way of determining results of games. If the values learnt were not the result of a search, then where did you get the values? The article stated that the values learnt were learnt without any domain specific knowledge. If this is so, then the only other possible way that you could obtain results is by searching or else you would have to traverse the whole chess tree or learn the values by piece handicaps but you didn't do this. In your match results section 5.1 you state that you verified the final values by playing matches beteen identical search engines. At what ply lookahead did these matches take place? Just because you randomized all candidate moves, doesn't obviate the need for knights being treated special when calculating values. In your experiment it is obvious to me that knights would be underated and from your results it certainly verifies this. You didn't specify whether the candidate moves were randomized in the matches. I will assume that they were not randomized. Of course your results from the special matches seem to back up the accuracy of the "new" values but this is misleading for the following reason. The accuracy of the other pieces has a very big bearing on the results. The accuracy of the traditional value of 5 points for the rook in relation to the other pieces has always been in question. an example of this is the materiel balance of 2R+N =2B+R. This has been known from the days of Spielmann. Your new point results more accurately reflect this balance. Also B+p vs R balance is more accurately reflected in your new balance. The advantage of the bishop is when you have both and this isn't reflected in your charts. It may be that since the knights are undervalued in your new figures, this reflects nicely in the power of the 2 bishops. So your new values by accident more accurately reflect the real values of the game when only considering one piece against another and not considering combinations of pieces. However your experiment could only value individual pieces, but i contend that your knights would't have died as many deaths if the search depth had been longer. However in that case perhaps your values results would have been less accurate as a whole when looking at all 5 piece values. it is interesting that the relation of the queen to the rook in your new values might be more realistic than the traditional values. all these materiel balance reasons could more than make up for your undervaluing the knight . All experiments so far with large statistical studies of bishop vs Knight suggest that their values are much much closer than the values you come up with. On May 22, 1998 at 15:33:00, Don Beal wrote: >On May 19, 1998 at 12:45:59, Komputer Korner wrote: > >>There was an article on refining piece values in the ICCA journal last >>year, but there was a major flaw in the research becuase they used >>lookahead search of only 4 plies and that is not enough for knight >>manoeuvers. > >The article was "Learning Piece Values Using Temporal Differences" >ICCA Journal, Vol 20, No 3, Sept 1997. > >I was concerned to find the inaccurate comment "major flaw in >the research". > >Whilst I don't think "inkompetent computers :-)" are likely to >damage reputations too seriously, I'd just like to respond... > >The intention of the paper was to present the learning *method*, >and its ability to start from no information whatsoever about piece >values and obtain effective ones. The values learnt were *not* >the result of the research; the fact that the method obtained >sensible values as opposed to ineffective values *was*. > >That said, however, the values were, of course, of interest, which >is why we reported them. > >We made no claim that the values would be the optimum values >for larger search depths, nor, as we commented in the paper, >optimum if the evaluation included positional terms. (Our >"proof of principle" experiment used piece values as the only >evaluation!) > >As regards the lookahead depth, we didn't try large depths, but >over the range of depths we did try, we found that the depth of >search had only a small effect on the values obtained. This is >perhaps less surprising when one realises that the learning method >reacts to *positions* and the associated game outcome, not to move >sequences that might, or might not, occur between the position and >the end of the game. > > >Other people are experimenting with TD learning - if any are >reading CCC, perhaps they have values for comparison? > >Don Beal.
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