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Indirect Encoding of Neural Networks for Scalable Go
[chapter]
2010
Parallel Problem Solving from Nature, PPSN XI
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
doi:10.1007/978-3-642-15844-5_36
dblp:conf/ppsn/GauciS10
fatcat:5hqylncw7fhyngu2s26feeiguu