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Generalisation over details: the unsuitability of supervised backpropagation networks for Tetris
Citation
Lewis, IJ and Beswick, SL, Generalisation over details: the unsuitability of supervised backpropagation networks for Tetris, Advances in Artificial Neural Systems, 2015 Article 157983. ISSN 1687-7594 (2015) [Refereed Article]
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Copyright Statement
Copyright 2015 I. J. Lewis and S. L. Beswick. This article is licensed under the terms of the Creative Commons Attribution License (CC BY 3.0 AU)
Abstract
We demonstrate the unsuitability of Artificial Neural Networks (ANNs) to the game of Tetris and show that their great strength,
namely, their ability of generalization, is the ultimate cause. This work describes a variety of attempts at applying the Supervised
Learning approach to Tetris and demonstrates that these approaches (resoundedly) fail to reach the level of performance of handcrafted
Tetris solving algorithms. We examine the reasons behind this failure and also demonstrate some interesting auxiliary
results. We show that training a separate network for each Tetris piece tends to outperform the training of a single network for
all pieces; training with randomly generated rows tends to increase the performance of the networks; networks trained on smaller
board widths and then extended to play on bigger boards failed to show any evidence of learning, and we demonstrate that ANNs
trained via Supervised Learning are ultimately ill-suited to Tetris.
Item Details
Item Type: | Refereed Article |
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Keywords: | neural networks, Tetris, machine learning |
Research Division: | Information and Computing Sciences |
Research Group: | Machine learning |
Research Field: | Neural networks |
Objective Division: | Information and Communication Services |
Objective Group: | Media services |
Objective Field: | Animation, video games and computer generated imagery services |
UTAS Author: | Lewis, IJ (Dr Ian Lewis) |
UTAS Author: | Beswick, SL (Mr Sebastian Beswick) |
ID Code: | 100063 |
Year Published: | 2015 |
Deposited By: | Information and Communication Technology |
Deposited On: | 2015-04-29 |
Last Modified: | 2017-11-01 |
Downloads: | 657 View Download Statistics |
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