By Aske Plaat, Walter Kosters, Jaap van den Herik
This booklet constitutes the completely refereed post-conference complaints of the ninth overseas convention on pcs and video games, CG 2016, held in Leiden, The Netherlands,in conjunction with the nineteenth desktop Olympiad and the twenty second international Computer-Chess Championship.
The 20 papers awarded have been conscientiously reviewed and chosen of 30 submitted papers.
The 20 papers hide quite a lot of computing device video games and plenty of diverse examine issues in 4 major sessions which made up our minds the order of book: Monte Carlo Tree seek (MCTS) and its improvements (seven papers), concrete video games (seven papers), theoretical facets and complexity (five papers) and cognition version (one paper). The paper utilizing Partial Tablebases in leap forward via Andrew Isaac and Richard Lorentz obtained the easiest Paper Award.
Read or Download Computers and Games: 9th International Conference, CG 2016, Leiden, The Netherlands, June 29 – July 1, 2016, Revised Selected Papers PDF
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Additional info for Computers and Games: 9th International Conference, CG 2016, Leiden, The Netherlands, June 29 – July 1, 2016, Revised Selected Papers
UCB1 slightly increased the number of discovered patterns, but its influence on agent’s performance was positive only for the American and British scoring systems. For the final agent we decided to use both evaluation schemes by alternating them with diﬀerent frequencies. In the evaluation phase, 5,000 simulated games are played using the set of hand pattern utilities learned in the training phase. The average of the games’ scores is used to evaluate the overall utility of a set of patterns. The set with the highest overall utility is used by the final agent.
We adopted a hybrid approach to obtain reliable distributions. A negamax-style backup of reward distributions is used in the shallower half of a search tree, and UCT is adopted in the rest of the tree. Experiments on synthetic trees show that this presented method outperformed UCT and similar methods, except for trees having uniform width and depth. 1 Introduction Monte Carlo tree search (MCTS) algorithms including UCT  have achieved remarkable success, especially in the game of Go . UCT is an algorithm based on the minimization of cumulative regret [1,13], which is suitable for estimating the expected score at each node.
Our empirical analysis indicated that the best search algorithms belong to the Monte Carlo and Greedy classes. As the final agent uses a combination of flat Monte Carlo and optimized greedy, we will present our implementation of these algorithms. Optimized Greedy implements a greedy strategy based on hand pattern utilities. Every new card placed on the tableau of face up cards, influences only two hands: the column and the row in which the card is placed. Thus, the value of placing the card in any position can be estimated by the sum of the changes of the values for the column hand and for the row hand.