Scalable neural networks for board games

Tom Schaul; Jürgen Schmidhuber

doi:10.1007/978-3-642-04274-4_103

Scalable neural networks for board games

Tom Schaul, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

Learning to solve small instances of a problem should help in solving large instances. Unfortunately, most neural network architectures do not exhibit this form of scalability. Our Multi-Dimensional Recurrent LSTM Networks, however, show a high degree of scalability, as we empirically show in the domain of flexible-size board games. This allows them to be trained from scratch up to the level of human beginners, without using domain knowledge. © 2009 Springer Berlin Heidelberg.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	1005-1014
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-04274-4_103
State	Published - Nov 19 2009
Externally published	Yes

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-642-04274-4_103

Cite this

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title = "Scalable neural networks for board games",

abstract = "Learning to solve small instances of a problem should help in solving large instances. Unfortunately, most neural network architectures do not exhibit this form of scalability. Our Multi-Dimensional Recurrent LSTM Networks, however, show a high degree of scalability, as we empirically show in the domain of flexible-size board games. This allows them to be trained from scratch up to the level of human beginners, without using domain knowledge. {\textcopyright} 2009 Springer Berlin Heidelberg.",

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N2 - Learning to solve small instances of a problem should help in solving large instances. Unfortunately, most neural network architectures do not exhibit this form of scalability. Our Multi-Dimensional Recurrent LSTM Networks, however, show a high degree of scalability, as we empirically show in the domain of flexible-size board games. This allows them to be trained from scratch up to the level of human beginners, without using domain knowledge. © 2009 Springer Berlin Heidelberg.

AB - Learning to solve small instances of a problem should help in solving large instances. Unfortunately, most neural network architectures do not exhibit this form of scalability. Our Multi-Dimensional Recurrent LSTM Networks, however, show a high degree of scalability, as we empirically show in the domain of flexible-size board games. This allows them to be trained from scratch up to the level of human beginners, without using domain knowledge. © 2009 Springer Berlin Heidelberg.

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UR - http://www.scopus.com/inward/record.url?scp=70449483091&partnerID=8YFLogxK

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DO - 10.1007/978-3-642-04274-4_103

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Scalable neural networks for board games

Abstract

ASJC Scopus subject areas

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