By H. Jaap van den Herik, Hiroyuki Iida, Aske Plaat
This publication constitutes the completely refereed post-conference lawsuits of the eighth overseas convention on pcs and video games, CG 2013, held in Yokohama, Japan, in August 2013, along with the seventeenth computing device and video games match and the 20 th global Computer-Chess Championship. The 21 papers provided have been rigorously reviewed and chosen for inclusion during this e-book. They hide a variety of themes that are grouped into 5 sessions: Monte Carlo Tree seek and its improvements; fixing and looking out; research of video game attribute; new methods; and critical games.
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Extra info for Computers and Games: 8th International Conference, CG 2013, Yokohama, Japan, August 13-15, 2013, Revised Selected Papers
An important one is the Rapid Action Value Estimation (RAVE), described for example in . This is an application of the all-moves-as-ﬁrst (AMAF) heuristic already introduced in the ﬁrst Monte-Carlo Go program in 1993 . The idea is to propagate a simulation result not only to parent nodes but also to sibling nodes corresponding to the moves played in the simulation. It is useful to boost the startup phase of the search with noisy but immediately available values. We have implemented the UCT algorithm in a Go program called Nomitan.
Figure 3 shows one of the test positions that contains two semeai, one on the upper right, the other on the lower right of the board. Black is to play and the result should be a clear win for white, hence a negative ﬁnal score, because both semeai can be won by the white player. Figure 3b shows the corresponding score histogram of 128,000 MC simulations. The colors indicate the clustering computed by the method described in Subsect. 2. Figure 3a, c, d, and e show the respective criticality values for the white player to end up in cluster 1, 2, 3, and 4, counted from left to right.
Finally, in Sect. 6, we discuss some other characteristics of our program that are possibly related to the eﬃciency of the proposed bias. 2 Monte-Carlo Tree Search The main idea of Monte-Carlo Tree Search (MCTS) is to construct a search tree step by step by repeatedly choosing the most promising child node of the already searched tree, expanding it, and then evaluating the new leaf with random selfplay simulations, often called playouts. The result of the simulations, usually a binary win or loss value, is back-propagated in all the parent nodes up to the root, and a new most promising leaf node is chosen.
Computers and Games: 8th International Conference, CG 2013, Yokohama, Japan, August 13-15, 2013, Revised Selected Papers by H. Jaap van den Herik, Hiroyuki Iida, Aske Plaat