Jason Gauci and Kenneth O. Stanley (2010)
Indirect Encoding of Neural Networks for Scalable Go
In: Proceedings of the 11th International Conference on Parallel Problem Solving From Nature (PPSN-2010). New York, NY: Springer (10 pages)
The game of Go has attracted much attention from the artificial intelligence community. A key feature of Go is that humans begin to learn on a small board, and then incrementally learn advanced strategies on larger boards. While some machine learning methods can also scale the board, they generally only focus on a subset of the board at one time. Neuroevolution algorithms particularly struggle with scalable Go because they are often directly encoded (i.e. a single gene maps to a single connection in the network). Thus this paper applies an indirect encoding to the problem of scalable Go that can evolve a solution to 5x5 Go and then extrapolate that solution to 7x7 Go and continue evolution. The scalable method is demonstrated to learn faster and ultimately discover better strategies than the same method trained on 7x7 Go directly from the start.