Lessons Learned from AlphaGo

The game of Go is known to be one of the most complicated board games. Competing in Go against a professional human player has been a long-standing challenge for AI. In this paper we shed light on the AlphaGo program that could beat a Go world champion, which was previously considered non-achievable for the state of the art AI.

[1]  Richard E. Korf,et al.  Depth-First Iterative-Deepening: An Optimal Admissible Tree Search , 1985, Artif. Intell..

[2]  D. A. Mechner,et al.  All Systems Go , 1998 .

[3]  John Tromp,et al.  Combinatorics of Go , 2006, Computers and Games.

[4]  Amos J. Storkey,et al.  Training Deep Convolutional Neural Networks to Play Go , 2015, ICML.

[5]  Hendrik Baier,et al.  Adaptive Playout Policies for Monte-Carlo Go , 2010 .

[6]  Pieter Spronck,et al.  Monte-Carlo Tree Search: A New Framework for Game AI , 2008, AIIDE.

[7]  Bernd Brügmann Max-Planck.  Monte Carlo Go , 1993 .

[8]  Martin Müller,et al.  Computer Go , 2002, Artif. Intell..

[9]  Rémi Coulom,et al.  Computing "Elo Ratings" of Move Patterns in the Game of Go , 2007, J. Int. Comput. Games Assoc..

[10]  Shirish Chinchalkar,et al.  An Upper Bound for the Number of Reachable Positions , 1996, J. Int. Comput. Games Assoc..

[11]  Csaba Szepesvári,et al.  Bandit Based Monte-Carlo Planning , 2006, ECML.

[12]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[13]  Shang-Rong Tsai,et al.  Current Frontiers in Computer Go , 2010, IEEE Transactions on Computational Intelligence and AI in Games.

[14]  Peter Lewis,et al.  MOVE ORDERING VS HEAVY PLAYOUTS : WHERE SHOULD HEURISTICS BE APPLIED IN MONTE CARLO GO ? , 2007 .

[15]  Jordan B. Pollack,et al.  Methods for statistical inference: extending the evolutionary computation paradigm , 1999 .

[16]  David Silver,et al.  Move Evaluation in Go Using Deep Convolutional Neural Networks , 2014, ICLR.

[17]  Yngvi Björnsson,et al.  Learning Simulation Control in General Game-Playing Agents , 2010, AAAI.

[18]  Bruno Bouzy,et al.  Computer Go: An AI oriented survey , 2001, Artif. Intell..

[19]  Simon M. Lucas,et al.  A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.

[20]  David Silver,et al.  Combining online and offline knowledge in UCT , 2007, ICML '07.

[21]  Michael L. Littman,et al.  Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.

[22]  Claudio Moraga,et al.  The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning , 1995, IWANN.

[23]  Ronald L. Wasserstein,et al.  Monte Carlo: Concepts, Algorithms, and Applications , 1997 .

[24]  Sylvain Gelly,et al.  Modifications of UCT and sequence-like simulations for Monte-Carlo Go , 2007, 2007 IEEE Symposium on Computational Intelligence and Games.

[25]  Liuqing Yang,et al.  Where does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond , 2016, IEEE/CAA Journal of Automatica Sinica.

[26]  Ronald J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.

[27]  Shih-Chieh Huang,et al.  Investigating the Limits of Monte-Carlo Tree Search Methods in Computer Go , 2013, Computers and Games.

[28]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[29]  Jan Willemson,et al.  Improved Monte-Carlo Search , 2006 .

[30]  T. Raiko.  Towards Super-Human Artificial Intelligence in Go by Further Improvements of AlphaGo Tapani Raiko , 2016 .

[31]  G. Palm.  Warren McCulloch and Walter Pitts: A Logical Calculus of the Ideas Immanent in Nervous Activity , 1986 .

[32]  Donald C. Wunsch,et al.  Computer Go: A Grand Challenge to AI , 2007, Challenges for Computational Intelligence.

[33]  Fredrik A. Dahl,et al.  Honte, a go-playing program using neural nets , 2001 .

[34]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[35]  Bruno Bouzy,et al.  Associating Shallow and Selective Global Tree Search with Monte Carlo for 9*9 Go , 2004, Computers and Games.

[36]  Gerald Tesauro,et al.  Monte-Carlo simulation balancing , 2009, ICML '09.

[37]  Donald E. Knuth,et al.  An Analysis of Alpha-Beta Pruning , 1975, Artif. Intell..

[38]  Rémi Coulom,et al.  Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search , 2006, Computers and Games.

[39]  Rémi Munos,et al.  Bandit Algorithms for Tree Search , 2007, UAI.

[40]  Bruno Bouzy,et al.  Move-Pruning Techniques for Monte-Carlo Go , 2006, ACG.