Watch an AI bot play Go [video]
facebook.comVery cool. I wrote a Go playing game for the Apple II which I sold.
I have recently become interested, again, in Go programming. I have started setting up a framework for using GnuGo to generate training data for a deep learning network. I am starting with a tiny 7x7 board just to see if the general approach works.
If it works (at all) then I will post the entire project to github.
Cool. I recently started working on a command line Go program. Mainly for learning Haskell and without any AI, yet. https://github.com/tsujigiri/gosh
Fun! I have a Haskell Go web app. It has a super experimental AI that can't actually win that I only spent a couple of hours on: https://github.com/tinco/ergo
I'll check that out!
Mark,
You should prototype it in Lisp :-)
What's interesting to me about this is the visual pattern recognition -- rather than just scaling up a giant Monte-Carlo search, playing like (presumably) humans do, searching for patterns, is very cool.
I was just this weekend going through my go library, and most of the books I have focus on precisely this sort of pattern training. In fact, there's a whole branch of study in go called in Japanese "Tesuji", essentially brilliant moves that come from bad-looking patterns.
The idea is that you lull your opponent into complacency with your bad-looking pattern, then "BAM", a surprise comes out. My guess is that an RNN could be discovering new tesuji, which would be super cool. If the RNN can look ahead deep enough, then I would think that's a path forward against strong players.
There's also the reverse, Anti-suji, essentially things that look brilliant, but are really terrible.
There is something captivating about watching two master-AI's battling it out on the Go board, it's almost Art in a way ;)
Facebook's AI lab will probably reveal more technical details at NIPS 2015. Here's the workshop on Reasoning, Attention, Memory (RAM) they are organizing that will seek to address some of the challenges around advancing from classification and prediction tasks to simple cognition:
Related article: https://code.facebook.com/posts/1478523512478471/teaching-ma...
From the short description, it looks like a project from Google about training a deep network to play the game. The resulting bot was quite strong (or at least, quite strong for not using Monte Carlo tree search, at least). Without any more details though, it's hard to tell.
Here is the paper for Google's DNN-based move evaluation in Go: http://www.cs.toronto.edu/~cmaddis/pubs/deepgo.pdf
Thanks, I wasn't sure where I had the reference.
Great! Thank you!
Implementing a Go player using neural networks and reinforcement learning is really fun and taught me a lot about how said techniques work. I only covered the (much easier) 9x9 board, but would recommend it as an exercise to those interested :)
I've had this in my backburner for some time already (I'm actually an avid 9x9 player and have lots of games already available for an initial training, but probably not enough to get a decent player just out of a neural network)
Do you have a link for the Google thing?
"strong human player." Like 1 Dan strong or like 6 kyu strong? It's a meaningless statement.
If it's comparable to other computer go ais then it's low dan.
Go should just be labeled Gogame in contrast to Golang.
> Go should just be labeled Gogame in contrast to Golang.
At the same time, there's something amusing about the idea that a six year-old programming language should cause a 2000 year old game to change its name.
It's also kind of weird we call it by its (newer) Japanese name instead of weiqi.
There was a reason for that though. Before the latter half of the 20th century Japanese go scene kind of dominated for several centuries (thanks to official shogun sponsor).
More importantly, until the past few decades, Japan did the majority of the go outreach in the West. That's changed a bit, and thanks to Korean influence, it's more common to hear people saying "baduk".
Weiqi is the transliteration we've arrived at now, but there have existed variants in the past. The Korean name Baduk is fairly unambiguous.
Search for the word Go on the internet is next to impossible.
Using the label gogame or baduk I think are adequate solutions. Also a go board is referred to as a goban.
Try searching for 碁. I'm sure you'll get better results. Interpretation is left as an exercise for the reader.
I always search for "go weiqi" when looking for the game, although "go baduk" is probably better.
Does that make the search engine GoOgle?
He used Monte Carlo methods, not DNN, but a nice talk on Computer Go by Zach Tellman
Btw, why is go-the-game capitalised? We don't capitalise Chess, do we?
that's a good answer: capitalize to disambiguate
After a few months of playing ... it's as good as a very strong human player.
This is potentially a really big breakthrough if true, as Go is much harder than Chess computationally. Nobody knows about Go in the West though so don't expect any headlines.
This is hard to evaluate. Explicit metrics would be good, but there's also a definitional point. Depending on who you ask, existing AI programs are as good as a very strong human player. Others will scoff at that characterization. The problem is what "very strong" means. Precisely, current programs can beat many professionals with a 4 stone handicap.
Making good analogies is hard, but here's mine: current go programs are probably somewhere in the range of runners running a 2:45 marathon. They're beyond what most people can hope to reach, perhaps even with intensive training, but they're not world-class. In fact, they're not just below world-class: there are thousands of people better than them who are themselves not yet world class. (Context: I play go and run at a level far below the ones quoted above, but know people who play/run at that level. And I've seen those people get their butts kicked by stronger players/runners).
Another easier comparison for anyone not familiar with how good 2:45 marathon is (like me). Right now current programs are around ~2500 ELO in chess, or about the weaker part of most International Grandmaster in chess.
You're right that the comparison won't work for everyone. Here's another: Franz-Josef-Dickhut, currently the 54th strongest player in Europe[1] just won 3-1 against Zen, a top program. He did the same last year against CrazyStone, another top program. There are strong European players, but much fewer than in China, Japan, Korea or Taiwan. So there are thousands, perhaps even ten thousand humans still stronger than the best programs.[2]
However, the ELO figure seems high to me. A go handicap stone is probably more than 100 ELO at those levels, because high level players are good at conserving small advantages. Additionally, the players in these exhibition matches are usually themselves not the top players in the world. For instance, Zhou Junxun or Ishida Yoshio are two I recall off the top of my head.[2]
[1] http://europeangodatabase.eu/EGD/createalleuro3.php?country=...
[2] If you know how many humans can run a 2:45 marathon, let me know! I know Boston lets you in with a 3:05 time if you're 18-34, and tens of thousands of people run that (some of them are older or women, but many eligible people don't compete...). I'm kinda spitballing on the exact times, but that's roughly how I want to do the comparison.
[3] (http://www.goratings.org/).
2:45 marathon would generally put you in the elite but non-professional class and give you a good shot of being in the top 10 for the smaller marathons. That's where many former collegiate runners would be able to do.
A good way to do comparison might be to do the percentile. 2:45 would put you in the 76 percentile.
Another thing to note is that the professionals that can be beaten with 4 handicap are usually pretty late in their career, and do not play as well as they did when they were in their prime. Much less at the level current pros in their prime play.
But, surely, they are going to improve the system, and fast.
I would bet that in a few years is going to be clear who is superior, the machine or the human.
That's been happening for a while now. My understanding is that there was a major wall many go AIs were stuck on prior to the invention of monte carlo tree search (essentially, a way of evaluating a better play objective function) and they're improving now again. There appears to be reticence to evaluate the strength of go programs, though.
Here's a chart of current ratings of popular engines on online forums. The rating system is one of "kyu" and "dan" going from 25k to 1k to 1d to 9d.
http://senseis.xmp.net/?KGSBotRatings
Informally there's another rating system the "pro" rating system which is a bit ceremonial but also broadly assumed to be often higher than the amateur dan rankings.
There was a huge improvement in the period of roughly 2005 through a few years ago, at which point programs seemed to be making only slow, incremental progress. At the current rate, it looks like we're talking at least a decade to reach full professional status.
I guess Facebook is claiming they're upending that, but that remains to be seen.
I spent several years in Asia and never met someone playing Go, just a few Chinese chess players. Would it really make headlines anywhere?
Yes. Something like ten million people in Korea play go. It's astonishingly popular there.
It's shown on TV in Korea, I believe
Does "To help teach systems how to plan, Facebook's AI research team has created an AI bot to play the board game Go. " actually teach AI how to "plan" at anything beyond board games?
This Facebook Video suggests it has had months of either training or development, which sounds might be it learning by playing, reinforcement learning in this space could be very interesting - more details would be good.
The two breakthrough papers using Deep Conv Nets to play Go - trained on Expert Games to predict the Experts Next Move.
This is made possible by the huge archives of expert play from online Go servers that have reached Big Data sizes in the last few years.
Teaching Deep Convolutional Neural Networks to Play Go - Clark & Storkey Edinburgh Informatics 2015 http://arxiv.org/abs/1412.3409
Move Evaluation in Go Using Deep Convolutional Neural Networks Maddison, Huang, Silver, Sutskever Deepmind 2015 http://arxiv.org/abs/1412.6564
David Silver has work on Reinforcement Learning and Go: Reinforcement Learning of Local Shape in the Game of Go Silver et al 2007. https://scholar.google.co.uk/citations?view_op=view_citation...
Go is scored by occupying and surrounding territory and the opponents pieces taken.
The current State of the Art programs use Monte Carlo Tree Search with each move being evaluated using random playouts - this is well suited to Go as it reasnoble estimate for comparison of the territory that will result from a move.
As the Go search tree branches a lot (~200 branches per play) tree search takes some time and cannot be exhaustive.
The Expert Move Predicting Convnets provide very good moves very quickly, and can provide and provide probabilities the expert would move for each square in a single forward pass.
Most of the current development now centers around using the Convnets to prune the search trees for the random playout MCTS engines.
Go is useful from an AI perspective because it is a huge state space and the patterns are subtle with long term implications. No computer can yet beat the best humans - so it is an area where natural intelligence beats artificial.
Yet the convnets are not doing planning, only predicting what an expert human would do next, the planning is implicit in the dataset but not a part of the convnet itself (though the internals of deepnets are mysterious so who knows).
The Computer Go mailing list is very lively and full of the best program creators with regular computer tournaments against each other. http://computer-go.org/mailman/listinfo/computer-go
The Facebook video shows GnuGo is the opposing player, this is not the best computer player and suggests Facebooks player is not as strong as the Convnets as yet - it also does not detail how deep the playouts of the tree search are, i.e. what level GnuGo is playing at, so this cannot really be compared to recent work.
Hopefully Facebook will publish more details soon - if they are learning through play then their results may well be interesting.
Remi Coulom's excellent slides detail the Monte Carlo revolution in Go programs. http://www.remi-coulom.fr/JFFoS/JFFoS.pdf
AFAIK MCTS with Random playout and hand coded heuristics are still SOTA.
When I visited in the summer Edinburgh Uni Informatics Department convnet Go was in active development.