daves@curie.ces.cwru.edu (David Stoutamire) (06/08/91)
A technical report is now available:
"Machine Learning, Game Play and Go", David Stoutamire,
Tech. Report TR 91-128, Center for Automation and Intelligent
Systems Research, Case Western Reserve University,
Cleveland, Ohio 44106.
Abstract:
The game of go is an ideal problem domain for exploring machine
learning: it is easy to define and there are many human experts,
yet existing programs have failed to emulate their level of play to
date. Existing literature on go playing programs and applications
of machine learning to games are surveyed. An error function based
on a database of master games is defined which is used to formulate
the learning of go as an optimization problem. A classification
technique called {\em pattern preference} is presented which is
able to automatically derive patterns representative of good moves;
a hashing technique allows pattern preference to run efficiently on
conventional hardware with graceful degradation as memory size
decreases.
This is more or less a subset of my thesis:
"Machine Learning Applied to Go", MS thesis,
David Stoutamire, Case Western Reserve University, 1991.
Postscript for the report is available from caisr2.caisr.cwru.edu
[129.22.24.22] as pub/iku/report.ps.Z, in compressed form. This is 90
pages of text. A photocopy can also be obtained by writing to the
center and asking for report TR 91-128.
Complete C++ source for the experiments described is available
in compressed, tared form as pub/iku/iku.tar.Z. This code requires
g++ and libg++ to compile. Included are a set of C++ classes to support
working with the game of go (Move, Board, Game) that may be useful
as a framework for programs out of the context of my thesis.
--
David Stoutamire daves@alpha.ces.cwru.edu