My Go program relies on machine learning to improve itself. An empirical approach would be to have a population of versions of a Go program, let the versions compete against each other, and choose the best versions to 'breed' and improve the population. Perhaps use genetic algorithms to exchange 'material' between the versions.

The advantage of this approach is that it is empirical, one doesn't need to collect training positions. The disadvantage is how long it would take even if the versions compete on smaller size boards to some extent. Unless the population is large there's a likelihood of inbreeding, the versions would only know how to play against the way they play. So I didn't go down this route.

Instead my Go program is taught much like a human player is taught, against sample problems and from collections of games. I have been typing in problems from books I own - though my books tend to be old and what's considered good changes. I also rely on game collections like *GoGoD'.

There are challenges in using human Go games as they stand for teaching, one is the games will contain mistakes. I'm hoping that the good moves will outweight the bad moves, but also that reading ahead will also compensate for any bad moves considered in particular positions.