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.
%0 Journal Article
%1 Stanley2004a
%A Stanley, Kenneth O.
%A Miikkulainen, Risto
%D 2004
%J Genetic and Evolutionary Computation - GECCO 2004
%K imported
%P 1226--1238
%T Evolving a Roving Eye for Go
%U http://www.springerlink.com/content/96y7lyycbj8k67ey
%V 3103
%X 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.
@article{Stanley2004a,
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.},
added-at = {2009-09-12T19:19:34.000+0200},
author = {Stanley, Kenneth O. and Miikkulainen, Risto},
biburl = {https://www.bibsonomy.org/bibtex/27c652c65e91f86c9947186ade54403cf/mozaher},
interhash = {9ca050a433c6b35a3ec11465155705cf},
intrahash = {7c652c65e91f86c9947186ade54403cf},
journal = {Genetic and Evolutionary Computation - GECCO 2004},
keywords = {imported},
owner = {Mozaher},
pages = {1226--1238},
timestamp = {2009-09-12T19:19:43.000+0200},
title = {Evolving a Roving Eye for Go},
url = {http://www.springerlink.com/content/96y7lyycbj8k67ey},
volume = 3103,
year = 2004
}