Erik van der Werf on AI Techniques for the Game of Go
Erik van der Werf came and visited the Applied Games group on November 27th/28th 2006. Erik gave a very interesting presentation with an overview of his work on computer Go. The focus of his work was on search techniques (solving 5x5 Go) and on machine learning techniques for territory and move prediction. Erik's homepage contains a lot of material on his work including his publications and an online version of his PhD thesis
We had a lot of interesting discussions, in particular on how to use machine learning to guide game tree search. Erik made us aware of the importance of life & death features for territory prediction. Also, he shared a lot of insights on search, in particular, the importance of transposition tables. He also played Go with our pattern-based Go engine Liberty. Fortunately, he treated Liberty with mercy and gave it nine handicap stones. Liberty ended up winning but also clearly demonstrated its lack of knowledge about life & death, search, and connectivity. If you are interested you can read our paper on Bayesian Pattern Ranking for Move Prediction in the Game of Go which describes the inner workings of Liberty.
Unfortunately, Erik is currently not working on computer Go professionally. Instead, he works on improving hearing aids at GN Resound, which involves challenging signal processing tasks on low-power processors. While he is continuing working on computer Go in his spare time, it would be great if he found his way back to full-time computer Go sometime in the future. After all, improved search and machine learning are two promising directions for computer Go.
Thore Graepel
