> 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). 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.
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