By Tristan Cazenave, Mark H.M. Winands, Hiroyuki Iida (eds.)
This booklet constitutes the refereed complaints of the pc video games Workshop, CGW 2013, held in Beijing, China, in August 2013, along with the Twenty-third foreign convention on man made Intelligence, IJCAI 2013. The nine revised complete papers provided have been rigorously reviewed and chosen from 15 submissions. The papers disguise quite a lot of themes regarding computing device video games. They speak about six video games which are performed by means of people in perform: Chess, Domineering, chinese language Checkers, pass, Goofspiel, and Tzaar. additionally, there are papers in regards to the Sliding Tile Puzzle, an program, particularly, Cooperative Path-Finding difficulties, and on common online game playing.
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Additional info for Computer Games: Workshop on Computer Games, CGW 2013, Held in Conjunction with the 23rd International Conference on Artificial Intelligence, IJCAI 2013, Beijing, China, August 3, 2013, Revised Selected Papers
The sampled counterfactual regret is an unbiased estimate of the counterfactual regret. In OOS, each simulation chooses a single exploration player iexp , which alternates across simulations. Also, the probability of sampling to a state s due to the exploring player’s selection policy, π, is maintained. These two parameters are added to the function in line 1 of Algorithm 1. Deﬁne σit (s), regret and average strategy tables as in Subsect. 3. Regret matching (Eq. 5) is used to build the strategies, and the action selected for i = iexp is sampled with probability ps,ai = γ/|A(s)| + (1 − γ)σit (s, ai ).
A backward induction method to solve PD-Goof(N ) was originally described in  and has recently been implemented and used to solve the game  for N ≤ 13, therefore the optimal minimax value for each state is known. Our evaluation makes use of these in Subsect. 3. However, WL-Goof(N ) is more common in the games and AI community [3,12,17,23]. Mixing between strategies is important in Goofspiel. Suppose a player does not mix and always bids with card n at s. An opponent can respond by playing card n + 1 if n = 13 and n = 1 otherwise.
LNCS (LNAI), vol. 4212, pp. 282–293. Springer, Heidelberg (2006) 17. : Sampling for regret minimization in extensive games. In: Advances in Neural Information Processing Systems (NIPS 2009), pp. 1078–1086 (2009) Monte Carlo Tree Search in Simultaneous Move Games 43 18. : Monte Carlo sampling and regret minimization for equilibrium computation and decision-making in large extensive form games. D. thesis, Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada (2013) 19.