I agree that the human brain is better wired to play Go than Chess. Humans can remember hundreds of thousands of visual images and their significance, but we only have a working memory of 6-7 steps in a temporal sequence. There are fewer possible moves in chess and its easier to assign point values to positions, so a computer can make moves based on expected consequences beyond 6-7 moves in the future.
In Go there are too many possible future combinations for even computer systems to deal with. Humans rely on pattern recognition for effective game play. Computers are extremely poor at pattern recognition of salient features and almost totally lacking in intermediate goal setting which are extremely important to human play. Computers are not really 'thinking' but are merely sorting data to come up with a 'simulation' of a proper game move. There are many board scenarios that can be presented to game systems that demonstrate they have absolutely no concept of the games. The newly popular technique of Monte Carlo Tree Search does go a step closer to a human thought process of predicting the consequences of moves to a distant future outcome rather than just calculating point values a few moves ahead which is perhaps why this technique has had such fantastic success lately. On Thu, Jun 3, 2010 at 5:45 PM, <[email protected]> wrote: > Send Computer-go mailing list submissions to > [email protected] > > To subscribe or unsubscribe via the World Wide Web, visit > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go > or, via email, send a message with subject or body 'help' to > [email protected] > > You can reach the person managing the list at > [email protected] > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Computer-go digest..." > > > Today's Topics: > > 1. Re: Chess vs Go // AI vs IA (Michael Williams) > 2. Re: Chess vs Go // AI vs IA (Mark Boon) > 3. Re: Chess vs Go // AI vs IA (Don Dailey) > 4. Re: Chess vs Go // AI vs IA (David Fotland) > 5. Re: Dynamic komi revisited (Petr Baudis) > 6. Re: Dynamic komi revisited (Petr Baudis) > 7. Re: Dynamic komi revisited (Darren Cook) > 8. Re: Dynamic komi revisited (Petr Baudis) > 9. Re: Dynamic komi revisited (Petr Baudis) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Wed, 02 Jun 2010 18:38:12 -0400 > From: Michael Williams <[email protected]> > To: [email protected] > Subject: Re: [Computer-go] Chess vs Go // AI vs IA > Message-ID: <[email protected]> > Content-Type: text/plain; charset=windows-1252; format=flowed > > One thing that never seems to get mentioned in this periodic debate is the > relationship, within the brain, of sight, patterns and memory. A go board > looks very > similar 10 or even 20 moves in the future. The same is not true for chess. > It looks vastly different, and in many cases would be over by that time. I > postulate that humans are able to read more situations more deeply in Go than > Chess because of the fact that much of the board is unchanged, visually and > therefor easier to remember. In chess, things move around and become harder > to remember. Computers have excellent memories and don't care about how > things > "look". This gives them an advantage over humans in games that are visually > "fast". See also: reversi/othello. I'm sure there are counter-examples. > It's > just something else to consider. > > > > > ------------------------------ > > Message: 2 > Date: Wed, 2 Jun 2010 13:01:26 -1000 > From: Mark Boon <[email protected]> > To: [email protected] > Subject: Re: [Computer-go] Chess vs Go // AI vs IA > Message-ID: > <[email protected]> > Content-Type: text/plain; charset=ISO-8859-1 > > On Wed, Jun 2, 2010 at 12:37 PM, Don Dailey <[email protected]> wrote: >> I don't require anything to be that precise, ? but I want statements to have >> a bit of substance. ? Phrases such as, "he is good" depends on a frame of >> reference. >> The best players in the world are not a good frame of reference either, >> ?they certainly do not represent humanity in general. ? ?And how good humans >> play is based on their culture and education too. >> So when we compare programs to humans, we usually mean some very well >> trained human, ?someone in the 95th percentile or something like that, ?not >> really a representative of human-kind. ? ?So which measuring stick do you >> consider to be "accurate" for comparing how computers (not humans) play >> completely different games? >> If you compare the average player, we have probably already succeeded - the >> best computer go programs are much better than the average go player. >> ?So now all we have to do is make progress and move up the ranks, ?just like >> humans have to do - and stop calling it hard. ? ?That is a given and is why >> we do it. ? ?I do computer chess for the same reason, ?it is very hard. > > I feel you are beating around the bush. Go is harder to program to > match a human expert than chess. Yes, no or you don't want to answer. > > We can probably define 'hard' in terms of time spent by humans trying, > but I hope we don't need to get that petty. > > I agree with Michael that the static nature of the go-board probably > helps humans to make things easier. But it's at least interesting that > that feature has not been equally exploited successfully by computers > or their programmers. > > Mark > > > ------------------------------ > > Message: 3 > Date: Wed, 2 Jun 2010 21:34:04 -0400 > From: Don Dailey <[email protected]> > To: [email protected] > Subject: Re: [Computer-go] Chess vs Go // AI vs IA > Message-ID: > <[email protected]> > Content-Type: text/plain; charset="iso-8859-1" > > I agree with you. Our brains are wired to play go better than chess. > > Don > > > On Wed, Jun 2, 2010 at 6:38 PM, Michael Williams < > [email protected]> wrote: > >> One thing that never seems to get mentioned in this periodic debate is the >> relationship, within the brain, of sight, patterns and memory. A go board >> looks very similar 10 or even 20 moves in the future. The same is not true >> for chess. It looks vastly different, and in many cases would be over by >> that time. I postulate that humans are able to read more situations more >> deeply in Go than Chess because of the fact that much of the board is >> unchanged, visually and therefor easier to remember. In chess, things move >> around and become harder to remember. Computers have excellent memories and >> don't care about how things "look". This gives them an advantage over >> humans in games that are visually "fast". See also: reversi/othello. I'm >> sure there are counter-examples. It's just something else to consider. >> >> >> >> _______________________________________________ >> Computer-go mailing list >> [email protected] >> http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >> > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: > <http://dvandva.org/pipermail/computer-go/attachments/20100602/d543756c/attachment-0001.html> > > ------------------------------ > > Message: 4 > Date: Wed, 2 Jun 2010 22:52:45 -0700 > From: "David Fotland" <[email protected]> > To: <[email protected]> > Subject: Re: [Computer-go] Chess vs Go // AI vs IA > Message-ID: <0a6c01cb02e0$fdae9cc0$f90bd6...@com> > Content-Type: text/plain; charset="us-ascii" > > I spent about six months writing such a program, about 8 years ago, to learn > alpha beta for use in my go program, and I doubt it's very strong. It > crushes me, but I'm really weak, maybe 1500. If someone wants to try some > games against it I can make the executable available. It uses iterative > deepening, partial ply extensions, killer heuristic, piece-square tables, > very simple pawn structure and king safety terms, etc. > > David > >> >> Out of interest, can you put a number on that? How strong would a >> program be, running on a modern fast desktop and using only alpha-beta >> and a static evaluation function based on, say, piece-square tables plus >> "a little consideration to position"? >> >> -M- >> _______________________________________________ >> Computer-go mailing list >> [email protected] >> http://dvandva.org/cgi-bin/mailman/listinfo/computer-go > > > > ------------------------------ > > Message: 5 > Date: Thu, 3 Jun 2010 11:23:58 +0200 > From: Petr Baudis <[email protected]> > To: [email protected] > Cc: Computer Go <[email protected]> > Subject: Re: [Computer-go] Dynamic komi revisited > Message-ID: <[email protected]> > Content-Type: text/plain; charset=us-ascii > > On Wed, Jun 02, 2010 at 03:27:11PM -0700, Peter Drake wrote: >> Okay, next question: on the previous thread ("Dynamic komi's >> basics"), there were several comments to the effect of, "I found an >> improvement from doing this and will describe it in an upcoming >> paper". Have any of these papers yet been produced or published? > > I'm still in the process of writing one up, but I'm not sure yet > if I will seek publishing it in any more formal venue, since the overall > results are rather disappointing. I still need to write it up for my > thesis anyway, though - I expect to have a draft ready in few weeks. > > In handicap games, the improvement is significant, but in even games, > the best method I have found gives only tiny statistically significant > improvement - about 54% winrate in self-play after very intensive > parameter tuning, with the effect being smaller in fast games and > somewhat more pronounced with large simulation counts. I was not able > to reproduce Hiroshi Yamashi's results with his (different) algorithm > either in my program. > > -- > Petr "Pasky" Baudis > The true meaning of life is to plant a tree under whose shade > you will never sit. > > > ------------------------------ > > Message: 6 > Date: Thu, 3 Jun 2010 11:23:58 +0200 > From: Petr Baudis <[email protected]> > To: [email protected] > Cc: Computer Go <[email protected]> > Subject: Re: [Computer-go] Dynamic komi revisited > Message-ID: <[email protected]> > Content-Type: text/plain; charset=us-ascii > > On Wed, Jun 02, 2010 at 03:27:11PM -0700, Peter Drake wrote: >> Okay, next question: on the previous thread ("Dynamic komi's >> basics"), there were several comments to the effect of, "I found an >> improvement from doing this and will describe it in an upcoming >> paper". Have any of these papers yet been produced or published? > > I'm still in the process of writing one up, but I'm not sure yet > if I will seek publishing it in any more formal venue, since the overall > results are rather disappointing. I still need to write it up for my > thesis anyway, though - I expect to have a draft ready in few weeks. > > In handicap games, the improvement is significant, but in even games, > the best method I have found gives only tiny statistically significant > improvement - about 54% winrate in self-play after very intensive > parameter tuning, with the effect being smaller in fast games and > somewhat more pronounced with large simulation counts. I was not able > to reproduce Hiroshi Yamashi's results with his (different) algorithm > either in my program. > > -- > Petr "Pasky" Baudis > The true meaning of life is to plant a tree under whose shade > you will never sit. > > > ------------------------------ > > Message: 7 > Date: Thu, 03 Jun 2010 18:36:38 +0900 > From: Darren Cook <[email protected]> > To: Computer Go <[email protected]> > Subject: Re: [Computer-go] Dynamic komi revisited > Message-ID: <[email protected]> > Content-Type: text/plain; charset=ISO-8859-1 > >>... results are rather disappointing. ... >> >> In handicap games, the improvement is significant, but in even games, >> the best method I have found gives only tiny statistically significant >> improvement - about 54% winrate in self-play... > > As the idea (at least, as I understood it) is for when the player > strength is unbalanced (i.e. handicap games, or playing an opponent who > is stronger in the opening but weaker in the endgame (or vice versa)) > that you'd get 54% from self-play in even games is intriguing. I look > forward to reading your draft paper. > > Darren > > > > -- > Darren Cook, Software Researcher/Developer > > http://dcook.org/gobet/ (Shodan Go Bet - who will win?) > http://dcook.org/work/ (About me and my work) > http://dcook.org/blogs.html (My blogs and articles) > > > ------------------------------ > > Message: 8 > Date: Thu, 3 Jun 2010 11:45:45 +0200 > From: Petr Baudis <[email protected]> > To: [email protected] > Cc: Computer Go <[email protected]> > Subject: Re: [Computer-go] Dynamic komi revisited > Message-ID: <[email protected]> > Content-Type: text/plain; charset=us-ascii > > On Thu, Jun 03, 2010 at 06:36:38PM +0900, Darren Cook wrote: >> >... results are rather disappointing. ... >> > >> > In handicap games, the improvement is significant, but in even games, >> > the best method I have found gives only tiny statistically significant >> > improvement - about 54% winrate in self-play... >> >> As the idea (at least, as I understood it) is for when the player >> strength is unbalanced (i.e. handicap games, or playing an opponent who >> is stronger in the opening but weaker in the endgame (or vice versa)) >> that you'd get 54% from self-play in even games is intriguing. I look >> forward to reading your draft paper. > > My idea is rather to better deal with "extreme situations" - when most > differences between various moves in a situation are so small that they > are lost in the noise (precise definition of when we consider the > situation extreme can vary, I explore multiple approaches). Then the > opening in handicap game would be a special case of this, but also any > other situation in the game where the program wins/loses by a lot. > > -- > Petr "Pasky" Baudis > The true meaning of life is to plant a tree under whose shade > you will never sit. > > > ------------------------------ > > Message: 9 > Date: Thu, 3 Jun 2010 11:45:45 +0200 > From: Petr Baudis <[email protected]> > To: [email protected] > Cc: Computer Go <[email protected]> > Subject: Re: [Computer-go] Dynamic komi revisited > Message-ID: <[email protected]> > Content-Type: text/plain; charset=us-ascii > > On Thu, Jun 03, 2010 at 06:36:38PM +0900, Darren Cook wrote: >> >... results are rather disappointing. ... >> > >> > In handicap games, the improvement is significant, but in even games, >> > the best method I have found gives only tiny statistically significant >> > improvement - about 54% winrate in self-play... >> >> As the idea (at least, as I understood it) is for when the player >> strength is unbalanced (i.e. handicap games, or playing an opponent who >> is stronger in the opening but weaker in the endgame (or vice versa)) >> that you'd get 54% from self-play in even games is intriguing. I look >> forward to reading your draft paper. > > My idea is rather to better deal with "extreme situations" - when most > differences between various moves in a situation are so small that they > are lost in the noise (precise definition of when we consider the > situation extreme can vary, I explore multiple approaches). Then the > opening in handicap game would be a special case of this, but also any > other situation in the game where the program wins/loses by a lot. > > -- > Petr "Pasky" Baudis > The true meaning of life is to plant a tree under whose shade > you will never sit. > > > ------------------------------ > > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go > > End of Computer-go Digest, Vol 5, Issue 7 > ***************************************** > _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
