[computer-go] ICML paper

[David Silver]

Hi all,

Our paper "Sample-Based Learning and Search with Permanent and  
Transient Memories" has been accepted for publication at ICML 2008.  
Once again, I would really appreciate any feedback or comments on the  
paper.

http://www.cs.ualberta.ca/~silver/research/publications/files/dyna2.pdf

ICML is a technical conference on machine learning, and the paper  
assumes some familiarity with machine learning terminology. Having  
said that, I hope that the main ideas are understandable by everyone  
on this list! In particular I've heard several people ask questions  
like:

-How can we generalise between different positions during search?
-How can we learn "on the fly" about the value of different patterns?
-How can we combine learned knowledge with search?
-Are there more efficient algorithms than Monte-Carlo for updating the  
value of states or patterns?
-What's so special about UCT? Can we get similar or better performance  
by using other sample-based search algorithms?

The Dyna-2 architecture described in this paper provides an approach  
for answering these questions.
Hopefully this will pique your interest enough to read the paper :-)

Thanks!
-Dave