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Subject: Re: chess and neural networks

Author: Ralph Stoesser

Date: 14:27:06 07/01/03

Go up one level in this thread

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)
>>>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?

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.

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