CrazyStone vs Aoba Kaori

Yet another Computer Go program wins a handicap game (8 stones) against a professional Go player. Earlier today, CrazyStone (running on a PC with 8 processors) played Aoba Kaori (4P) at the FIT2008 conference (here are some photos) and won by resignation.

In a couple of weeks, MoGo plays Myungwan Kim (again) at the Cotsen Go Tournament. It will be interesting to see whether or not they can repeat the success from the US Go Congress. I have a feeling that Kim will have adapted to MoGo's playing style.

MoGo beats Myungwan Kim (8P) at US Go Congress

From the AGA newsletter (via the ComputerGo mailing list):

COMPUTER BEATS PRO AT U.S. GO CONGRESS:
In a historic achievement, the MoGo computer program defeated Myungwan Kim 8P (l) Thursday afternoon by 1.5 points in a 9-stone game billed as “Humanity’s Last Stand?” “It played really well,” said Kim, who estimated MoGo’s current strength at “two or maybe three dan,” though he noted that the program – which used 800 processors, at 4.7 Ghz, 15 Teraflops on a borrowed European supercomputer – “made some 5-dan moves,” like those in the lower right-hand corner, where Moyogo took advantage of a mistake by Kim to get an early lead. “I can’t tell you how amazing this is,” David Doshay -- the SlugGo programmer who suggested the match -- told the E-Journal after the game.

“I’m shocked at the result. I really didn’t expect the computer to win in a one-hour game.” Kim easily won two blitz games with 9 stones and 11 stones and minutes and lost one with 12 stones and 15 minutes by 3.5 points. The games were played live at the U.S. Go Congress, with over 500 watching online on KGS. “I think there’s no chance on nine stones,” Kim told the EJ after the game. “It would even be difficult with eight stones. MoGo played really well; after getting a lead, every time I played aggressively, it just played safely, even when it meant sacrificing some stones. It didn’t try to maximize the win and just played the most sure way to win. It’s like a machine.”

The game generated a lot of interest and discussion about the game’s tactics and
philosophical implications. “Congratulations on making history today,” game organizer Peter Drake told both Kim and Olivier Teytaud, one of MoGo’s programmers, who participated in a brief online chat after the game. At a rare loss for words in a brief interview with the EJ after the game, Doshay wondered “How much time do we have left? We’ve improved nine stones in just a year and I suspect the next nine will fall quickly now.”

- reported by Chris Garlock

Amazing! Though, the last quote (by David Doshay) should probably be taken with a grain of salt.

[Update 2008-08-09] Apparently, David was misquoted.

Human-Computer 19x19 Go Showdown

From the Computer-Go mailing list:

On Thursday, August 7, at 1:00 PM (Pacific time), Kim MyungWan 8p will take on MoGo, the world’s strongest computer Go program. MoGo will connect remotely from France, where it will be running on a supercomputer boasting over 3,000 processor cores. The game will be broadcast on KGS.

Sweet! I guess I'll get an answer to some questions quite a bit sooner than I expected.

AI -TV from 1984

José of Programming Musings found a video with Daniel Bobrow explaining CLOS from 1987. Being somewhat of a history-geek, I really love this type of thing and I've managed to collect a few interesting links myself.

One that I can particularly recommend is Computer Chronicles: Artifical Intelligence (1984) which is a twenty minute TV show featuring, among others, John McCarthy and discusses AI in general, Expert Systems and Lisp.

Common Lisp Reasoner

I just found the SourceForge project Common Lisp Reasoner. It's a CLOS extension for AI applications like scheduling and diagnosis. I haven't had time to check it out, but it looks interesting. I wonder if it plays nicely with other CLOS extensions, like the production system Lisa for example.

Evolvable Rules

I stumbled on Evolvable Rules and REAT (Rete Evolution of Augmenting Topologies) yesterday whilst searching for interesting stuff to read. I'm not yet sure about what I think of the project but Greg sure got my attention.

The idea behind evolvable rules seem to be using Artificial Neural Network (ANN) techniques to generate a Rete network and some additional stuff like conflict resolution and the code needed for executing the RHS of a rule.

There's not that much info yet but Greg shares a few references that explain the technology he intends to use. Apart from Charles Forgy's Rete papers (the thesis and the article) and the Wikipedia description of the Rete algorithm (which we have another Charles to thank for) he also refers to Evolving Neural Networks through Augmenting Topologies by Kenneth O. Stanley and Risto Miikkulainen.

I haven't read the last paper that thoroughly (skimmed it once) and I'm only just learning about ANN technology but it seems to me that this requires a lot of work. And, if I understand correctly, it might not be very good at producing a Rete network at all and since there are "simple" rules that can be used to construct a Rete network based on, for example, an Abstract Syntax Tree I don't really see the point. Apart from being really cool of course ;-)

Anyway, I'm real interested to see how all this works out. I've had similar lines of thought myself after having read An Optimization Algorithm for Production Systems by Toru Ishida last summer. It would be really neat to find a way to automatically optimize (restructure) a Rete network based on the contents of the working memory without having to pass through all Facts and re-evaulate the goal of the optimization continuously.

The problem is that I have no idea of how, or even if, it can be accomplished. But I bet that if someone does it, it's probably going to have something to do with using ANN GA techniques. I'm not holding my breath though.