#!/usr/bin/env python
# coding: utf-8

# # Modern Game Playing

# ## Monte-Carlo Tree Search
# 
# Szita, I., Chaslot, G.M.J-B., and Spronck, P. (2010). [Monte-Carlo Tree Search in Settlers of Catan](https://link.springer.com/content/pdf/10.1007%2F978-3-642-12993-3_3.pdf). Advances in Computer Games (ACG 2009) (eds. H.J. van den Herik and P.H.M. Spronck), Vol. 6048 of LNCS, pp. 21–32, Springer-Verlag, Berlin Heidelberg, Germany
# 
# [Monte-Carlo Tree Search](https://pdfs.semanticscholar.org/9457/b87afbdadd49b5936f694582d16c08cb7ee4.pdf), thesis by Guillaume  Chaslot, 2010.
# 
# [A Survey of Monte Carlo Tree Search Methods](http://mcts.ai/pubs/mcts-survey-master.pdf), by C.B. Browne, et al.,2012
# 
# [Examples](http://www.mcts.ai/)

# ## Computer Chess
# 
# [Wikipedia on Computer Chess](http://en.wikipedia.org/wiki/Computer_chess)
# 
# [Giraffe: Using Deep Reinforcement Learning to Play Chess](https://arxiv.org/pdf/1509.01549.pdf), by Matthew Lai
# 
# [Deep-Pink](https://github.com/erikbern/deep-pink) by Erik Bernhardsson, and corresponding [blog post](http://blog.yhat.com/posts/deep-learning-chess.html)
# 
# [DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess](http://www.cs.tau.ac.il/~wolf/papers/deepchess.pdf), by Omar David, et al.

# ## Negamax on GPU
# 
# [Enhanced parallel NegaMax tree search algorithm on GPU](http://ieeexplore.ieee.org/document/6972394/?section=abstract), by Ahmed A. Elnaggar, et al.
# 
# 

# ## Go
# 
# [Mastering the game of Go with deep neural networks and tree search](https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf), by David Silver, et al., Nature, vol. 529, 28 January 2016 
# 
# Read all about it at [DeepMind's site on AlphaGo](https://deepmind.com/research/alphago/)

# [CUDA Implementation of Computer Go Game Tree Search](https://link.springer.com/content/pdf/10.1007%2F978-3-319-32467-8_31.pdf), by Christine Johnson