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Subject: Re: chess and neural networks

Author: Ralph Stoesser

Date: 08:57:36 07/02/03

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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)
>>>>>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.
>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.

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.


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