Evolving a roving eye for go

Abstract

Go remains a challenge for artificial intelligence. Currently, most machine learning methods tackle Go by playing on a specific fixed board size, usually smaller than the standard 19×19 board of the complete game. Because such techniques are designed to process only one board size, the knowledge gained through experience cannot be applied on larger boards. In this paper, a roving eye neural network is evolved to solve this problem. The network has a small input field that can scan . boards of any size. Experiments demonstrate that (1) The same roving eye architecture can play on different board sizes, and (2) experience gained by playing on a small board provides an advantage for further learning on a larger board. These results suggest a potentially powerful new methodology for computer Go: It may be possible to scale up by learning on incrementally larger boards, each time building on knowledge acquired on the prior board. © Springer-Verlag Berlin Heidelberg 2004.