Author: Ralph Stoesser
Date: 08:57:36 07/02/03
Go up one level in this thread
On July 02, 2003 at 10:46:03, Marc van Hal wrote: >On July 01, 2003 at 17:27:06, Ralph Stoesser wrote: > >>On July 01, 2003 at 17:08:29, Marc van Hal wrote: >> >>>On July 01, 2003 at 16:17:37, Magoo wrote: >>> >>>>On July 01, 2003 at 16:02:14, Albert Bertilsson wrote: >>>> >>>>>On July 01, 2003 at 15:55:07, Anthony Cozzie wrote: >>>>> >>>>>>On July 01, 2003 at 15:42:42, Albert Bertilsson wrote: >>>>>> >>>>>>>>Yes, but things are different with chess. In backgammon, you don't need to do >>>>>>>>deep searches. Backgammon is a randomized game, chess is not. There have been >>>>>>>>attempts, but not that succesful, i have looked at KnightCap, which uses >>>>>>>>standard minimax with a ANN to evaluate the quiet positions.It has a rating of >>>>>>>>about 2200 at FICS... pretty good, but no way near the top. I guess a program >>>>>>>>with minimax only counting material would have a rating near that. Like they >>>>>>>>say, chess is 99% Tactics. Nothing beats deeper searching. >>>>>>> >>>>>>>2200 on FICS with MiniMax counting material only? >>>>>>> >>>>>>>That is crazy! >>>>>>> >>>>>>>One of us is wrong, and hope it isn't me because I've spent many hours on my >>>>>>>engine and it still is now way near 2200 in anything other than Lightning! If >>>>>>>you're right I'm probably the worst chess programmer ever, or have missunderstod >>>>>>>your message completely. >>>>>>> >>>>>>>/Regards Albert >>>>>> >>>>>> >>>>>>Your engine, being new, still has a lot of bugs. I'm not trying to insult you; >>>>>>it took me a full year to get my transposition table right. At least, I think >>>>>>its right. Maybe. Anyway, the point is that it takes quite a while to get a >>>>>>good framework. I suspect on ICC a program with PST evaluation only could get >>>>>>2200 blitz. (with material evaluation only it would play the opening horribly, >>>>>>e.g. Nc3-b1-c3-b1-c3 oh darn I lose my queen sort of stuff) >>>>>> >>>>>>Anthony >>>>> >>>>>I agree that PST evaluation with Alpha-Beta and a transposition-table can play >>>>>at least decent chess, but that's quite many powerful improvements over MiniMax >>>>>with Material only. >>>>> >>>>>/Regards Albert >>>> >>>>I said near, and when i say minimax, i really mean alphabeta (no one uses a >>>>straightforward minimax). When my engine was "born" (minimardi) it had only >>>>material evaluation, searching 4 ply, it could play a decent game. Rated around >>>>1700 blitz at FICS. Now, consider searching around 8 ply, i think a rating >2000 >>>>is not hard to imagine. My point was that in chess, the most important thing to >>>>accuretly evaluate positions is a deep search. No matter what methods you use, >>>>if you search deep your program will play decent. This is one of the reasons why >>>>ANN have worked so well in backgammon and not in chess. >>> >>>Can't neural networks look deep ? >>>Why is that? >>>And do neural networks learn or not? >>> >>>Marc >> >>No to the first question in any case and no to the second question in respect of >>Snowie backgammon. >>NN backgammon programs like Snowie are looking max. 3 ply ahead and evaluating >>the 'MiniMaxed' positions with a pre-trained NN. They do not learn anymore while >>playing, but it would be also possible to do so. > >What is neural networks if it does not learn by it self? >(a bugy program?) >And again: Why can't it look deep? > >I think real A.I. can not have these problems. > >Marc The NN does learn by itself, that's the idea of a NN! But in case of Snowie (and Jellyfish and GnuBackgammon) the NN had learned before. It has been trained enough (hopefully) by the developers of the program. A NN cannot look deep, but it can learn an evaluation function. In such games as chess, othello, checkers and so on you have a deep looking part (alpha-beta MiniMax) and an evaluation part. A NN can learn the evaluation function, the deep looking part is apart from it. Ralph
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