By Aske Plaat, Walter Kosters, Jaap van den Herik
This booklet constitutes the completely refereed post-conference court cases of the ninth foreign convention on pcs and video games, CG 2016, held in Leiden, The Netherlands,in conjunction with the nineteenth computing device Olympiad and the twenty second international Computer-Chess Championship.
The 20 papers provided have been rigorously reviewed and chosen of 30 submitted papers.
The 20 papers hide quite a lot of computing device video games and lots of assorted examine themes in 4 major sessions which decided the order of e-book: Monte Carlo Tree seek (MCTS) and its improvements (seven papers), concrete video games (seven papers), theoretical elements and complexity (five papers) and cognition version (one paper). The paper utilizing Partial Tablebases in leap forward by way of Andrew Isaac and Richard Lorentz got the easiest Paper Award.
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Extra info for Computers and Games: 9th International Conference, CG 2016, Leiden, The Netherlands, June 29 – July 1, 2016, Revised Selected Papers
Progressive strategies for Monte-Carlo tree search. New Math. Nat. Comput. 4(3), 343–357 (2008) 4. : cuDNN: eﬃcient primitives for deep learning (2014). http://arxiv. 0759 5. : Training deep convolutional neural networks to play go. In: Proceedings of The 32nd International Conference on Machine Learning, pp. 1766–1774 (2015) 6. : Combining online and oﬄine knowledge in UCT. In: Proceedings of the 24th International Conference on Machine Learning (ICML 2007), New York, NY, USA, pp. 273–280 (2007) 7.
196–207. Springer, Heidelberg (2012). 1007/ 978-3-642-31866-5 17 7. : Automated discovery of searchextension features. , Spronck, P. ) ACG 2009. LNCS, vol. 6048, pp. 182–194. Springer, Heidelberg (2010). 1007/978-3-642-12993-3 17 8. : Retrograde analysis of certain endgames. ICGA J. de Abstract. Deep Convolutional Neural Networks have revolutionized Computer Go. Large networks have emerged as state-of-the-art models for move prediction and are used not only as stand-alone players but also inside Monte Carlo Tree Search to select and bias moves.
1 −5 [−35, 25] ELO vs. 0 310 [271, 358] 696 2,456 Playouts/s The number of playouts per move was chosen as 11,000 for Abakus and 27,500 for Pachi. This is approximately the same amount of playouts which each program can achieve in 1 s on an empty board. In this way the experiments are comparable to later experiments which use 1 s thinking time. The results are shown in Table 2. The better the DCNN is the stronger the program plays against Pachi. But we can also see that the strength improvement declines for the last DCNNs.
Computers and Games: 9th International Conference, CG 2016, Leiden, The Netherlands, June 29 – July 1, 2016, Revised Selected Papers by Aske Plaat, Walter Kosters, Jaap van den Herik