Author: Vincent Diepeveen
Date: 14:10:59 03/24/03
Go up one level in this thread
On March 24, 2003 at 11:34:53, Robert Hyatt wrote: >On March 24, 2003 at 10:43:22, Vincent Diepeveen wrote: > >>On March 23, 2003 at 01:00:47, Robert Hyatt wrote: >> >>bob i explained the TD learning here. not crafty. i didn't knock >>against crafty at all. > >Just look at your reference to crafty. Unnecessary to even name an opponent. It was needed to explain why TD did score a few points. It scored it because crafty had a very weak king safety at the time, which explains why by rude tuning, the TD learning could give away full pieces for a few checks and of course we must not forget that i with diep searched like 6 ply at the time. with crafty you got 7-8 ply and knightcap got also 7 or 8 ply even. Against nowadays depths this would not score a single point simply. In short the 'noise' created by a major lack of software at the time it managed to get a few points, in combination with a search depth that was more than ok at the time. So further in time those learning algorithms proved worse than they looked at the time. > >> >>i was talking about the many games knightcap-crafty to show why i find the >>results drawn from the TD learning experiment are overreacted. > >This is yet another "it is impossible because I can't see how to make it work" >type >of discussion? _nothing_ sayd TD won't work. It _hasn't_ worked real well, so You go too far now Hyatt. You are just busy giving criticism against me, where i know you do not have personally a coin believe in TD learning anyway. I remember some hard postings of you there some years ago. >far, but then again full-width search didn't work in 1970 either. But it does >now. feel free to believe in the unknown random learners. i hope you realize that it is just at random flipping a few values and then retests. > > > >> >>i could have said thebaron-knightcap as well, but it played many games against >>crafty. >> >>you focus too much upon the word crafty here. focus upon the original question >>of the poster which is: "learning?" >> >> >>>On March 22, 2003 at 15:10:52, Vincent Diepeveen wrote: >>> >>>>On March 22, 2003 at 07:21:26, emerson tan wrote: >>>> >>>>>I heard that deep blue uses automatic tuning for its evaluation function, does >>>>>this mean that as it plays games against humans and computers, deep blue will >>>>>self tune its evaluation function base on the results of its games against >>>>>humans and computer? If it is, is it effective? Are the other programs using >>>>>automatic tuning also? >>>> >>>>Many tried tuning automatic, but they all failed. Human tuning is way better and >>>>more accurate. Big problems of autotuners is the number of experiments needed >>>>before a good tuning is there. >>>> >>>>Basically you only can proof a good tuning by playing games against others. >>>> >>>>That will in short take another few thousands of years to tune a complex >>>>evaluation. >>>> >>>>Doesn't take away that everyone has tried a bit in that direction and probably >>>>will keep trying. >>>> >>>>Current algorithms simply do not work. >>>> >>>>Also the much praised TD learning is simply not working. >>>> >>>>What it did was overreact things in the long term. So for example at the time >>>>that crafty was at a single cpu pro200 and those versions had a weak king safety >>>>some years ago, it would find out that weakness of crafty not as we conclude it. >>>>It just concluded that it could by sacraficing pieces and such towards crafty >>>>king, that this would work a bit. >>> >>> >>> >>>Why don't you spend more time talking about _your_ program and less time >>>knocking mine? Crafty 1996 (pentium pro 200) did just as well against >>>Diep 1996 as Crafty of today does against Diep of today. If my king safety >>>was weak in 1996 so was yours. >>> >>>Why don't you give up this particular path of insults? It only makes you >>>look idiotic. That "weak" version of Crafty in 1996 finished in 4th place >>>at the WMCCC event. Where did yours finish that year? In fact, I'd bet >>>that KnightCap had a better record against _your_ program that it did >>>against Crafty. Which makes your comparison all the more silly. >>> >>>Again, you should spend more time working and less time knocking other >>>programs. Your program would get better, faster. >>> >>> >>> >>>> >>>>but especially 'a bit' is important here. It of course would have been happy >>>>scoring 20% score or so. So if you gamble 10 games in a row and win 2 in that >>>>way and without a chance lose the others, then that might seem to work, but in >>>>absolute terms you are doing a bad job, because scoring 20% sucks. >>>> >>>>Of course those were the days that in 5 0 or 5 3 blitz levels the programs got >>>>very small search depths. Not seldom computer-computer games were tactically >>>>dominated in these days around 1997. >>>> >>>>Concluding that pruning works based upon those slaughter matches (where the >>>>knightcap stuff got butchered many games in a row then winning 1 game for it by >>>>some aggressive sacrafice) is not the right conclusion IMHO. >>>> >>>>Note other selflearning experts have more criticism against TD learning which i >>>>do not share too much. Their criticism is that some stuff is hard coded, so the >>>>tuner can't go wrong there. For me that 'cheating' is a smart thing to do >>>>however, because it is clear that tuning without domain knowledge isn't going to >>>>work within a year or 100. >>>> >>> >>>The DB guys didn't claim to do TD learning or any other _automated_ learning >>>whatsover. They claimed to have an evaluation _tuning_ tool that did, in fact, >>>seem to work. >>> >>>One problem is that when you change an eval term to correct one flaw, you can >>>introduce other bad behavior without knowing it. They tries to solve this by >>>the least-squares summation over a bunch of positions so that you could >>>increase something that needed help without wrecking the program in positions >>>where it was already doing well. >>> >>>The idea has (and still has) a lot of merit... Just because nobody does it >>>today doesn't mean it is (a) bad, (b) impossible, or (c) anything else. >>> >>> >>> >>>>In later years when hardware became faster, also evaluations became better >>>>without clear weak chains. >>>> >>>>Evaluations without clear weak chains are very hard to automatically tune. >>>> >>>>Basically tuners have no domain knowledge, so if you have a couple of thousands >>>>of patterns, not to mention the number of adjustable parameters, it will take >>>>more time than there are chess positions, to automatically tune them. >>>> >>>>And it is sad that the much praised TD learning, which completely sucked >>>>everywhere from objective perspective, is praised so much as a big step. >>>> >>>>Basically TD learning demonstrates that someone *did* do effort to implement TD >>>>learning and we can praise the person in question for doing that. >>>> >>>>Most 'learning' plans do not leave the paper ever. >>>> >>>>But having seen hundreds of games from knightcap i definitely learned that >>>>tuning without domain knowledge is really impossible. >>>> >>>>A result from those paper learning in AI world is next. Chessprograms improve >>>>and improve, but also get more complex. To list a few of the stuff programs >>>>might simulatenously have (without saying A sucks and B is good): >>>> - alfabeta >>>> - negamax >>>> - quiescencesearch >>>> - hashtables >>>> - multiprobing >>>> - complex datastructure >>>> - nullmove >>>> - possible forms of forward pruning >>>> - killermoves >>>> - move ordering >>>> - SEE (qsearch, move ordering) >>>> - futility >>>> - lazy evaluation >>>> - quick evaluation >>>> - psq tables to order moves >>>> - probcutoff >>>> - reductions >>>> - forward pruning (one of the many forms) >>>> - iterative deepening >>>> - internal iterative deepening (move ordering) >>>> - fractional ply depth >>>> - parallel search algorithms >>>> - check extensions >>>> - singular extensions >>>> - mating extensions >>>> - passed pawn extensions >>>> - recapture extensions >>>> - other extensions (so many the list is endless) >>>> >>>>This is just what i could type within 2 minutes. In short. All kind of >>>>algorithms and methods get combined to something complex and more complex and it >>>>is all 'integrated' somehow and some domain dependant; so requiring a lot of >>>>chess technical code some in order to work well. >>>> >>>>Because 99.9% of all tuning algorithms do not leave the paper, they usually can >>>>be described in a few lines of pseudo code. For that reason most are for 99.9% >>>>doing the same similar thing in the same way, but have a new cool name. Just >>>>like nullmove R=2 and R=3 is exactly the same algorithm (nullmove), but just a >>>>small implementation detail is different. >>>> >>>>Yet the simplicity of the AI-learning world is so small, most concepts are >>>>simply paper concepts which do not work in the real world. >>>> >>>>If they ever leave the paper then they perform a silly experiment or conclude >>>>things in the wrong way. Never objective science is getting done there. >>>> >>>>Those scientists hate programming simple. So the possibility to combine methods >>>>and especially combine them with domain dependant knowledge is like near zero. >>>>In that way TD learning is seen as a grown up algorithm. >>>> >>>>The good thing of it, is that it is doing something without crashing. The bad >>>>thing of it is that it left the paper so we could see how poor it worked. >>>> >>>>It would be too hard to say that the work has been done for nothing. In >>>>contradiction. It simply proofs how much paper work the AI is now and how people >>>>can believe in the unknown. >>>> >>>>I have met at least 100 students who wanted to make a selflearning thing. Either >>>>neural network, genetic algorithm etc. >>>> >>>>In fact in all those years only 1 thing i have seen working and that was a >>>>genetic algorithm finding a shorter path than my silly brute force search did >>>>(we talk about hundreds of points). That genetic algorithm however had domain >>>>dependant knowledge and was already helped with a short route to start with... >>>> >>>>Best regards, >>>>Vincent
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