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Evolving Indirectly Encoded Convolutional Neural Networks to Play Tetris With Low-Level Features

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dc.contributor.author Schrum, Jacob
dc.date.accessioned 2018-12-10T15:57:48Z
dc.date.available 2018-12-10T15:57:48Z
dc.date.issued 2018-07
dc.identifier.citation Jacob Schrum. 2018. Evolving indirectly encoded convolutional neural networks to play tetris with low-level features. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18), Hernan Aguirre (Ed.). ACM, New York, NY, USA, 205-212. DOI: https://doi.org/10.1145/3205455.3205459 en_US
dc.identifier.uri http://hdl.handle.net/11214/215
dc.description.abstract Tetris is a challenging puzzle game that has received much attention from the AI community, but much of this work relies on intelligent high-level features. Recently, agents played the game using low-level features (10 X 20 board) as input to fully connected neural networks evolved with the indirect encoding HyperNEAT. However, research in deep learning indicates that convolutional neural networks (CNNs) are superior to fully connected networks in processing visuospatial inputs. Therefore, this paper uses HyperNEAT to evolve CNNs. The results indicate that CNNs are indeed superior to fully connected neural networks in Tetris, and identify several factors that influence the successful evolution of indirectly encoded CNNs. en_US
dc.language.iso en_US en_US
dc.publisher Proceedings of the Genetic and Evolutionary Computation Conference en_US
dc.subject Computing methodologies en_US
dc.subject Machine learning en_US
dc.subject Machine learning approaches en_US
dc.subject Neural networks en_US
dc.subject Bio-inspired approaches en_US
dc.subject Generative and developmental approaches en_US
dc.title Evolving Indirectly Encoded Convolutional Neural Networks to Play Tetris With Low-Level Features en_US
dc.type Article en_US


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