Title
Indirect Encoding Of Neural Networks For Scalable Go
Abstract
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 x 5 Go and then extrapolate that solution to 7 x 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 x 7 Go directly from the start. © 2010 Springer-Verlag.
Publication Date
11-12-2010
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
6238 LNCS
Issue
PART 1
Number of Pages
354-363
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1007/978-3-642-15844-5_36
Copyright Status
Unknown
Socpus ID
78149249937 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/78149249937
STARS Citation
Gauci, Jason and Stanley, Kenneth O., "Indirect Encoding Of Neural Networks For Scalable Go" (2010). Scopus Export 2010-2014. 502.
https://stars.library.ucf.edu/scopus2010/502