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

Socpus ID

78149249937 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/78149249937

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