On Mon, 2008-08-11 at 12:23 -0400, Robert Waite wrote: > > Yes, but "exhausitve search" does not improve your player by 63% (eg.) > > for a doubling in CPU time. > > This part was done in an empirical scalability study. Please check the > > > archives of the list. > > > In the (inifinite) limit minimax+evaluation-function would find the > > perfect move > > too, but UCT/MC already find "good" moves before the limit. > Yes... I agree... UCT/MC seems to find the good moves before the limit > and from statistics.. seems that the good moves come out long before > we have exhaustively searched the tree. I was questioning the rate at > which we approach "perfect play". This term seems silly to me... as it > would imply actually solving the game. The whole idea of playing vs. > god and drawing or winning only means one thing to me... and that > would be actually knowing every possible path to determine the best > path. The results of the MC statistics simply say that this move > appears to be better given the sample size. To me.. I don't think > anyone could say that you could beat god without actually knowing the > whole tree. That would be conjecture at least at this point. And > having God in the equation already moves us to mysticism (or some sort > of statement that the game has a solution).
You don't need to know the whole tree, you only need to know some of the tree and it's a very small fraction of the whole. That's what alpha/beta pruning is all about. - Don > > As far as the 63% gain... I feel that there are certain additional > descriptors needed there. We did not see a statistical increase in > ability vs. human players. We saw a 63% gain when putting programs > against programs. This is hardly the same problem. It is valuable > information and I am not discounting it at all. I just feel that this > evidence DNE what it seemed to be used for in previous discussions. > > > ...Why are you trying to share it with us in the first place. > > For myself, i believe that what you are trying to do, is to > > begin to analyses all the data the community has gathered so far... > > Well.. things certainly got heated and as I looked at the list.. I > started feeling guilty that I kind of took over. The list seems > primarily used for coordination between you guys and perhaps at times > theoretical discussion. I apologize for the rants that have perhaps > shown up suddenly. > > The background reason I came in here was that I love go and have loved > it ever since I learned to play about 5 years ago. I am also a > developer and long ago had read many articles on computer go. At the > time.. and perhaps up to now.. there have been many go players, > computer scientists and lay people who have worried that perhaps the > greatest strength of the computer, fast computation, would not be such > a great help with playing go. There were taunts from this side saying > that computers couldn't really beat children who were decent. After > reading and hearing these sorts of discussions... I started to fall > into that group. My personal feeling was that AI now is akin to a > human taking a lot of time trying to create a particular algorithm. > Then this algorithm would work in a particular scenario. This seems > difficult for go as each of these heuristics are focused and > meanwhile, you have a human who is constantly changing his heuristics > during their years of learning. > > I feel that to have what movies consider "AI" or what the general > public expects from "AI", we will need a new paradigm where computers > learn to solve problems by themselves through experimentation and > learning. This does not necessarily apply to go, but is possible. > > The reason I brought up complexity theory is not to confine computer > go to a particular complexity class... but to discuss the fact that > our current model of computing machines do appear to solve many > important problems.. but that there some classes of problems that we > are not so certain can be solved with the computer model we all have > at our desks or in our datacenters. > > When I read the article by the DeepBlue guy called "Cracking Go", I > was very skeptical. I felt that he was assuming too much. When I read > that Mogo was going to get a nice big cluster.. I was very excited and > couldn't wait to watch the game. When Mogo started to turn around... I > had completely swtiched from skeptic to cheering it on. I think the > Mogo team and many people on here have done a great job. > > So then I jumped into conversation here and perhaps had not fully > researched previous topics and breakthroughs... but I felt that I was > cut down pretty quickly with the phrase "proven to be scalable to > perfect play". The phrase itself was used to completely nullify my > argument. That is perhaps where it started to get out of hand. Don's > "Duck" does not really seem to be clearly a duck. In his analogy... > his duck is almost an axiom and I am some crazy freak who thinks the > world is flat. I felt it was a bit condescending and did feel I had to > try to clear the logic up. > > At this point.. I have read the Bandit paper and am pretty sure where > he got this phrase. In the paper it is phrased differently. I am > probably at fault here because I have just jumped in here and have not > been a part of much previous discourse. Perhaps that phrase has a > different meaning here and people would assume what he meant. When I > saw that phrase... the first thing I thought was that they surely > meant practical. Afterall... what use is something that takes more > memory that we have in the universe and more time than the age of the > universe. Obviously we are hoping that it gets somewhere good well > before that... but the phrasing in the original paper did not seem to > use this phrase to show why it is practical. > > I looked at the empirical evidence (at least what I think is being > referred to).. and to me it does not overwhelmingly show that this can > be practically scaled to beat humans. I just don't see the duck... and > I don't think that is from having a weak intellect or flawed logic. It > seems that they are datapoints that are valuable in computer go... but > they are datapoints that I feel do not prove or even begin to prove > how Mogo will scale against humans. I don't think that the experiment > in this case has covered the model. > > My reasons to start discussing things on here was that I was curious > about the future of computer go. As a few discussions got heated.. I > felt that some weak logic was being thrown at me so I probably got a > little heated and started firing back. I will try to keep such > discussions out of the list. > > I have however enjoyed reading many people's responses and from all of > this... I have started getting much deeper into complexity theory... > from my schooling.. we only knew about big O notation and how to apply > it to our code. > > > > > > > _______________________________________________ > computer-go mailing list > computer-go@computer-go.org > http://www.computer-go.org/mailman/listinfo/computer-go/ _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
