It is well known that the game of go poses a special challenge to computer
technology, from modeling to programming, from Artificial Intelligence to
hardware. Despite three decades of world wide effort and recent progress with
Monte Carlo type go programs it is still possible for young children of Dan
level playing strength to beat the strongest programs running on clusters
with 100s of nodes.
The talk will describe the dynamic creation of a model that aims at
describing board positions in go. The model exploits the partially local
nature of go (more precisely of the capture rule in go) and leads in its
simple version to a polynomial dynamical systems for over 300 unknowns which
to formulate needs computer algebra support. Better models require the
solution of hybrid discrete-polynomial systems.
The talk will further report on the numerical solution of these dynamical
systems. A demo shows the real time operation of such a model and compares
its move predictions to moves made in professional games.