Before monte carlo I spent a couple of years writing and tuning an alpha-beta searcher. It's still in there and I ship it to provide the lower playing levels. Alpha-beta with limited time makes much prettier moves than monte carlo.
Would there be interest in a paper that compares the same knowledge and engine used in an alpha-beta and monte carlo framework? David > -----Original Message----- > From: computer-go-boun...@computer-go.org [mailto:computer-go- > boun...@computer-go.org] On Behalf Of Olivier Teytaud > Sent: Thursday, September 24, 2009 4:45 AM > To: computer-go > Subject: [computer-go] IEEE T-CIAIG Special Issue on Monte Carlo > Techniques and Computer Go > > IEEE Transactions on Computational Intelligence and AI in Games > > Special Issue on Monte Carlo Techniques and Computer Go > > Special-issue editors: Chang-Shing Lee, Martin Müller, Olivier Teytaud > > In the last few years Monte Carlo Tree Search (MCTS) has > revolutionised Computer Go, with MCTS programs such as MoGo, Crazy > Stone, Fuego, Many Faces of Go, and Zen achieving a level of play that > seemed unthinkable only a decade ago. These programs are now > competitive at a professional level for 9x9 Go, and with an 8 stone > handicap for 19x19 Go. > > The purpose of this special issue is to publish high quality papers > reporting the latest research covering the theory and practice of > these and other methods applied to Go, and also in applying MCTS to > other games. > > MCTS can play very well even with little knowledge about the game as > evidenced by its success in General Game Playing. However, it does not > work well for all games, which poses some interesting questions. When > and why does it succeed and fail? How can it be extended to new > applications where it does not work yet? How best may it be combined > with other approaches such as classical minimax search and > knowledge-based methods? > > Topics include but are not limited to: > > l Emergent Technologies for Computer Go > > l Variants of Go (phantom Go, Go Siege) > > l Knowledge Representation Models for Computer Go > > l MCTS and Reinforcement Learning > > l MCTS for Video Games > > l Approximation Methods for MCTS > > l MCTS for General Game Playing > > l Hybrid MCTS Approaches > > l Evolving MCTS Players > > > > Authors should follow normal T-CIAIG guidelines for their submissions, > but clearly identify their papers for this special issue during the > submission process. See http://www.ieee-cis.org/pubs/tciaig/ for > author information. Extended versions of previously published > conference papers are welcome providing the journal paper provides a > significant extension of the conference paper, and is accompanied by a > covering letter explaining the additional contribution. > > Schedule > > · Deadline for submissions: March 15, 2010 > > · Notification of Acceptance: June 15, 2010 > > · Final copy due: October 20, 2010 > > · Publication: December 2010 or March 2011 > _______________________________________________ > 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/
