Table of Contents
Advances in Artificial Neural Systems
Volume 2015 (2015), Article ID 157983, 8 pages
http://dx.doi.org/10.1155/2015/157983
Research Article

Generalisation over Details: The Unsuitability of Supervised Backpropagation Networks for Tetris

School of Engineering and ICT, University of Tasmania, Private Bag 87, Sandy Bay, TAS 7001, Australia

Received 19 January 2015; Accepted 1 April 2015

Academic Editor: Matt Aitkenhead

Copyright © 2015 Ian J. Lewis and Sebastian L. Beswick. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Pajitnov, Tetris, Spectrum HoloByte, Alameda, Calif, USA, 1985.
  2. P. Franklin, At 25, Tetris Still Eyeing Growth, 2009, http://www.reuters.com/article/2009/06/02/us-videogames-tetris-idUSTRE5510V020090602.
  3. W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biophysics, vol. 5, pp. 115–133, 1943. View at Google Scholar · View at MathSciNet
  4. S. Russel and P. Norvig, Artificial Intelligence A Modern Approach, Pearson Education, Cranbury, NJ, USA, 3rd edition, 2010.
  5. A. E. Bryson and Y. C. Ho, Applied Optimal Control: Optimization, Estimation, and Control, Blaisdell, Waltham, Mass, USA, 1969.
  6. G. F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Addison Wesley, Essex, UK, 2004.
  7. J. Brzustowski, Can you win at Tetris? [M.S. thesis], Department of Mathematics, University of British Columbia, 1992.
  8. V. F. Farias and B. V. Roy, “Tetris: a study of randomized constraint sampling,” in Probabilistic and Randomized Methods for Design under Uncertainty, pp. 189–201, Springer, London, UK, 2006. View at Publisher · View at Google Scholar
  9. R. Breukelaar, E. D. Demaine, S. Hohenberger, H. J. Hoogeboom, W. A. Kosters, and D. Liben-Nowell, “Tetris is hard, even to approximate,” International Journal of Computational Geometry & Applications, vol. 14, no. 1-2, pp. 41–68, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  10. S. W. Golomb, Polyominoes, Allen & Unwin, 1965.
  11. S. Melax, Reinforcement Learning Tetris Example, 1998, http://www.melax.com/tetris.html.
  12. C. P. Fahey, Tetris, 2003, http://colinfahey.com/tetris/tetris.html.
  13. I. El-Ashi, El-Tetris—An Improvement on Pierre Dellacherie's Algorithm, 2011, http://ielashi.com/el-tetris-an-improvement-on-pierre-dellacheries-algorithm/.
  14. Y. Bdolah and D. Livnat, Reinforcement Learning Playing Tetris, 2000, http://www.math.tau.ac.il/∼mansour/rl-course/student_proj/livnat/tetris.html.
  15. T. Hashieda and K. Yoshida, “Online learning system with logical and intuitive processings using fuzzy Q-learning and neural network,” in Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, vol. 1, pp. 13–18, IEEE, July 2003. View at Publisher · View at Google Scholar
  16. D. Harter and R. Kozma, “Task environments for the dynamic development of behavior,” in International Conference Computational Science (ICCS '01), vol. 2074 of Lecture Notes in Computer Science, pp. 300–309, Springer, Berlin, Germany, 2001. View at Publisher · View at Google Scholar
  17. S. Girgin and P. Preux, “Feature discovery in reinforcement learning using genetic programming,” in Genetic Programming, vol. 4971 of Lecture Notes in Computer Science, pp. 218–229, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  18. S. J. Sarjant, Creating reinforcement learning tetris AI [Bachelor of computer graphic design with honours], University of Waikato, Hamilton, New Zealand, 2008.
  19. K. Driessens and S. Džeroski, “Integrating guidance into relational reinforcement learning,” Machine Learning, vol. 57, no. 3, pp. 271–304, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  20. A. Grob, J. Friedland, and F. Schwenker, “Learning to play Tetris applying reinforcement learning methods,” in Proceedings of the European Symposium on Artificial Neural Networks—Advances in Computational Intelligence and Learning, Bruges, Belgium, April 2008.
  21. G. Tesauro, “Temporal difference learning and TD-gammon,” Communications of the ACM, vol. 38, no. 3, pp. 58–68, 1995. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Bellman, Adaptive Control Processes: A Guided Tour, Princeton University Press, Princeton, NJ, USA, 1961. View at MathSciNet
  23. D. Carr, Applying reinforcement learning to Tetris [Bachelor of Science (Honours) Thesis], Department of Computer Science, Rhodes University, Grahamstown, South Africa, 2005.
  24. N. Lundgaard and B. McKee, Reinforcement Learning and Neural Networks for Tetris, 2006, http://www.cs.ou.edu/~amy/courses/cs5033_fall2007/Lundgaard_McKee.pdf.
  25. S. Nissen, Fast Artificial Neural Network Library (FANN), 2009, http://leenissen.dk/fann/wp/.