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Subject: Re: chess and neural networks

Author: Marc van Hal

Date: 07:46:03 07/02/03

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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



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