Author: Vincent Diepeveen
Date: 06:24:58 07/07/03
Go up one level in this thread
On July 07, 2003 at 07:14:39, Ralph Stoesser wrote: I remember a time that in computerchess some very scientific university programs won the world title. That was when the programs could search like up to 9 ply or so. Sincethen Rebel won the open world title and it was a PC program from then on. This year the only scientific program joining at more than 1 processor will be DIEP. however because there has been fulltime work performed at the engine for quite some time and i doubt whether you can see it as only scientific. Not using the money generating ICGA definition, but the time invested, then the world title will be won by a pro for sure this year. It was like that the last 13 years too. That is because they can earn their living creating a chess engine. In short when search depths will go deeper a bit than they are now, and when it would be possible to make a living selling a backgammon engine, then all the amateuristic ANN crap will be gone for sure. Note that sometimes you can only sell your stuff saying you do the same like the competitor. I do not know to which amount that is the case in the wordings of the backgammon people. Just like Ed Schroder sold Rebel one day using 'anti-GM' feature. Until today we do not know what it is, except that he denied it being a simple thing like opening the position a bit more. It possibly is a commercial vehicle to sell something already existing, that's all. Note that other sports people claim that just search depth solved chess, referring to deep blue. So i get impression you are the same type of guy in this case. >On July 07, 2003 at 05:50:43, Vincent Diepeveen wrote: > >>On July 07, 2003 at 01:49:50, Ralph Stoesser wrote: >> >>Go to an average backgammon tournament and you'll see in the top many >>chessplayers there. of course don't try that in Greece. Not enough chessplayers >>there. But even there the few chessplayers will be winning the tournament >>nearly. > >Is this true for the very best bg players in the world, let's say for the top >20? > >> >>The doubling cube action is something that hand tuning will completely outgun of >>course. > >It gets a bit offtopic since we are talking about backgammon all the time, but >to clarify one thing about the cube handling: Accurate doubling cube action >depends strictly on accurate evaluation of the related position. The math for >the cube action _behind_ the board evaluation is relatively simple and for sure >nothing for a NN based tuning, but as a precondition to calculate an accurate >cube handling you need an accurate evaluation number of the current position, >where evaluation number means the average outcome in winning points when playing >the position in question infinite times. And in the field of evaluation of >backgammon positions it has been found (so far) that NN based evaluation tuning >does it better than hand tuned evaluation. This is obvious because otherwise the >best bg programs would not do it NN based. > >greetings, >Ralph > >> >>You must compare it with casino games. As soon as there is a lot of money >>involved all the chances there get written down by *hand* even. Also when it's >>thousands of possibilities. >> >>Now in games of backgammon, a lot of money at the topboards is involved, but >>that's because of people betting at their games, not getting paid for an engine. >> >>Cheers, >>Vincent >> >>>On July 06, 2003 at 21:26:31, Vincent Diepeveen wrote: >>> >>>>On July 06, 2003 at 17:51:53, Ralph Stoesser wrote: >>>> >>>>>On July 06, 2003 at 17:38:01, Vincent Diepeveen wrote: >>>>> >>>>>>On July 06, 2003 at 16:21:05, Uri Blass wrote: >>>>>> >>>>>>>On July 06, 2003 at 15:42:25, Vincent Diepeveen wrote: >>>>>>> >>>>>>>>On July 06, 2003 at 08:00:48, Uri Blass wrote: >>>>>>>> >>>>>>>>>On July 06, 2003 at 03:04:07, Christophe Theron wrote: >>>>>>>>> >>>>>>>>>>On July 06, 2003 at 01:15:41, Uri Blass wrote: >>>>>>>>>> >>>>>>>>>>>On July 06, 2003 at 00:25:49, Uri Blass wrote: >>>>>>>>>>><snipped> >>>>>>>>>>>>>Maybe using it for the evaluation is not the most efficient use of a neural >>>>>>>>>>>>>network in a chess program. It seems that the way human players manage to search >>>>>>>>>>>>>the tree is vastly underestimated. >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> Christophe >>>>>>>>>>>> >>>>>>>>>>>>I agree with you that search is underestimated in chess but I also believe >>>>>>>>>>>>that search and evaluation are connected because a lot of search decisions are >>>>>>>>>>>>based on evaluation of positions that are not leaf positions so you cannot >>>>>>>>>>>>seperate them and say search improvement gives x elo and evaluation improvement >>>>>>>>>>>>gives y elo. >>>>>>>>>>>> >>>>>>>>>>>>Uri >>>>>>>>>>> >>>>>>>>>>>I know that you did not try to seperate between them but my point is that if you >>>>>>>>>>>want to do the same as humans in the search then changing the search is not >>>>>>>>>>>enough. >>>>>>>>>>> >>>>>>>>>>>Humans may search position for some seconds and decide that this position is not >>>>>>>>>>>good and later search the same position but decide that it is good for them not >>>>>>>>>>>because they search deeper but because they learned to change their evaluation >>>>>>>>>>>based on searching other lines that leaded to a similiar position. >>>>>>>>>>> >>>>>>>>>>>Uri >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>>Well my point is just that when people talk about an application of ANN in chess >>>>>>>>>>they always talk about implementing the evaluation with an ANN, or tuning the >>>>>>>>>>evaluation with them. >>>>>>>>>> >>>>>>>>>>I think it tends to show that the application of ANN to chess has never been >>>>>>>>>>done by a "real" chess programmer. Because evaluation is only a part of a chess >>>>>>>>>>program. And maybe not the one that can be improved dramatically, or that needs >>>>>>>>>>them in order to be improved. Personally I would not use ANNs in the evaluation >>>>>>>>>>first, because I think they would be much more efficient somewhere else. >>>>>>>>>> >>>>>>>>>>On the other hand, you are right. If one could design an ANN to perform the >>>>>>>>>>evaluation, it would be wise to use the same ANN (or an extension of it) to >>>>>>>>>>guide the search. >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> Christophe >>>>>>>>> >>>>>>>>>I believe that the biggest advantage that can be achieved in evaluation is not >>>>>>>>>in changing the initial static evaluation but in learning to change the >>>>>>>>>evaluation during the game based on the results of the search. >>>>>>>>> >>>>>>>>>I also do not believe that what humans know is the target and the target should >>>>>>>>>be better than what humans know. >>>>>>>>> >>>>>>>>>programs found better evaluation than humans in backgammon and program may find >>>>>>>>>better search rules than humans in chess not because programs are smarter but >>>>>>>>>because programs may do trillions of calculation to learn and humans cannot do >>>>>>>>>it. >>>>>>>>> >>>>>>>>>Uri >>>>>>>> >>>>>>>>This is the same utter nonsense crap that i keep seeing AI people write. Yet on >>>>>>>>average they even have less experience than you and keep believing in something >>>>>>>>they can never proof to be made. If they would have even *toyed* with ANNs a bit >>>>>>>>they will understand more about the impossibilities about it. >>>>>>> >>>>>>>I only say that I believe that it can be done. >>>>>>>It does not mean that I know how to do it. >>>>>>> >>>>>>>> >>>>>>>>Show me a backgammon program with an ANN that beats a 5 turns fullwidth >>>>>>>>searching backgammon program :) >>>>>>>> >>>>>>>>Of course show it at a machine that you and i have at home. >>>>>>> >>>>>>>Very easy >>>>>>>the 5 turns fullwidth searching backgammon program is going to lose on time >>>>>>>every game. >>>>>>> >>>>>>> >>>>>>> >>>>>>>> >>>>>>>>The average ANN expert is assuming he has to his availability something doing >>>>>>>>10^1000 calculations. >>>>>>> >>>>>>>I am not ANN expert and I did not suggest ideas how to do it. >>>>>>> >>>>>>>> >>>>>>>>That is the major problem when talking to these guys. >>>>>>>> >>>>>>>>Of course you can optimize an ANN for chess in 10^1000 calculations. >>>>>>>> >>>>>>>>But you will then be beaten by a database of just 10^43. >>>>>>>> >>>>>>>>I am however sure that 99% of all ANN interested will not understand what i >>>>>>>>write here above, simply because they do not know the running time of the learn >>>>>>>>methods applied. If they would read themselves into that, then less crap would >>>>>>>>leave their mouth. >>>>>>> >>>>>>>I did not say that the learning methods that are used in backgammon can work in >>>>>>>chess and it is possible that people need to invent different learning methods. >>>>>>>Uri >>>>>> >>>>>>If there was money to earn by programming a backgammon engine, i am sure some >>>>>>guys who are good in forward pruning algorithms like Johan de Koning would win >>>>>>every event there. It's like making a tictactoe program and then claiming that >>>>>>an ANN is going to work. >>>>> >>>>>Version 4 Professional edition, full version USD 380 >>>>>from http://www.snowie4.com/ >>>>> >>>>>Do you know the rules of Backgammon? Remember, you have to consider two dices in >>>>>your search tree. If it's so easy to do better without NN, do it and you will >>>>>earn a lot of USD. Usually backgammon players have more mony in their pocket >>>>>than chess players ;) >>>> >>>>There is so little backgammon players however. >>> >>> >>> >>>If you go to a backgammon >>>>tournament i pay like 250 euro entry fee. it is sick. Every good chessplayer can >>>>play backgammon very well trivially. >>>> >>>>It is a matter of a good % calculation and chances. this is trivial stuff. >>> >>>It isn't trivial. How do you explain that all top backgammon programs use NNs? >>>Shouldn't be some trivial statistically calculation enough? In backgammon you >>>have not only the problem to find the best move (what is also not trivially), >>>but to find the right cube action for the doubling cube and that's very very far >>>from beeing trivial. And why a good chessplayer should be able to play very well >>>backgammon trivially? I would agree that it can help learning beackgammon to be >>>a good chessplayer, but there is nothing like the implication you gave about it. >>> >>> >>>If >>>>there was to earn big bugs with just ENGINE (so i do not mean interface) then >>>>there would be much chessprogrammers writing such an engine ;) >>> >>>Btw: >>>Is there big bucks to earn with a chess engine? >>> >>>> >>>>>Ralph >>>>> >>>>> >>>>> >>>>>> >>>>>>As we have a saying here: "In the land of the blind, one eyed is King". >>>>>> >>>>>>That's why i focus upon chess. >>>>>> >>>>>>In contradiction to you, i know how to do it with ANNs (just like many others >>>>>>do), i just don't have 10^1000 system time to actually let the learning >>>>>>algorithm finish ;) >>>>>> >>>>>>Any approximation in the meantime will be playing very lousy chess... >>>>>> >>>>>>Hell, with 10^1000 runs, even TD learning might be correctly finding the right >>>>>>parameter optimization :) >>>>>> >>>>>>TD learning is randomly flipping a few parameters each time. It's pretty close >>>>>>to GA's in that respect.
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