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Ghory, I. (2004). Reinforcement learning in board games (Technical Report CSTR-04-004, De-
partment of Computer Science, University of Bristol). Retrieved from http://www.cs.bris.ac.uk/
Publications/Papers/2000100.pdf.
LeCun, Y., Bottou, L., Orr, G., , & Müller, K. (1998). Efficient backprop. In Orr, G. & Müller, K.
(Eds.), Neural Networks: Tricks of the Trade , volume 1524 (pp. 5-50). Berlin: Springer.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representation by error
propagation. In Rumelhart, D. E. & McClelland, J. L. (Eds.), Parallel Distributed Processing:
Exploration in the Microstructure of Cognition . Cambridge, MA: MIT Press.
Sutton, R. S. & Barto, A. G. (1998). Reinforcement Learning . Cambridge, MA: MIT Press.
Tesauro, G. (1992). Practical issues in temporal difference learning. Machine Learning , 8(3-4),
257-277.
Tesauro,
G. (2002).
Programming backgammon using self-teaching neural nets.
Artificial
Intelligence , 134(1-2), 181-199.
Werbos, P. J. (1974). Beyond regression: New tools for prediction and analysis in the behavioural
sciences . Unpublished PhD dissertation, Harvard University, Cambridge, MA.
Wiering, M. A. (1995). TD learning of game evaluation functions with hierarchical neural architec-
tures . Unpublished masters thesis, Department of Computer Science, University of Amsterdam,
Amsterdam, Netherlands.
Wiering, M. A. (2010). Self-play and using an expert to learn to play backgammon with temporal
difference learning. Journal of Intelligent Learning Systems & Applications , 2(2), 57-68.
Wiering, M. A., Patist, J. P., & Mannen, H. (2007). Learning to play board games using
temporal difference methods (Technical Report UU-CS-2005-048,
Institute of Informa-
Retrieved from http://www.ai.rug.nl/ ~
tion and Computing Sciences,
Utrecht University).
mwiering/group/articles/learning_games_TR.pdf.
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