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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Indirect Encoding of Neural Networks for Scalable Go
verfasst von : Jason Gauci, Kenneth O. Stanley
Erschienen in: Parallel Problem Solving from Nature, PPSN XI
Verlag: Springer Berlin Heidelberg
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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 5×5 Go and then
extrapolate
that solution to 7×7 Go and continue evolution. The scalable method is demonstrated to learn faster and ultimately discover better strategies than the same method trained on 7×7 Go directly from the start.