Download Computers and Games: 6th International Conference, CG 2008, by Maarten P. D. Schadd, Mark H. M. Winands, H. Jaap van den PDF

By Maarten P. D. Schadd, Mark H. M. Winands, H. Jaap van den Herik (auth.), H. Jaap van den Herik, Xinhe Xu, Zongmin Ma, Mark H. M. Winands (eds.)

This ebook constitutes the refereed court cases of the sixth overseas convention on pcs and video games, CG 2008, held in Beijing, China, in September/October 2008 co-located with the thirteenth desktop Olympiad and the sixteenth international Computer-Chess Championship.

The 24 revised complete papers provided have been conscientiously reviewed and chosen from forty submissions. The papers conceal all points of synthetic intelligence in computer-game taking part in facing many various learn themes, reminiscent of cognition, combinatorial video game idea, seek, wisdom illustration, and optimization.

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Additional resources for Computers and Games: 6th International Conference, CG 2008, Beijing, China, September 29 - October 1, 2008. Proceedings

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An Analysis of UCT in Multi-player Games 39 Two algorithms have been introduced that attempt to deal with imperfect opponent models. The soft-maxn algorithm [9] uses a partial ordering over game outcomes to analyze games. It returns sets of maxn values at each node, with each maxn value in the set corresponding to a strategy that the opponents might play in a subtree. The prob-maxn algorithm [5] uses sets of opponent models and with probabilistic weights which are used for back-up at each node according to current opponent models.

Computing “Elo Ratings” of Move Patterns in the Game of Go. ICGA Journal 30(4), 198–208 (2007) 7. : Move Ordering vs Heavy Playouts: Where Should Heuristics Be Applied in Monte Carlo Go? In: Proceedings of the 3rd North American Game-On Conference (2007) 8. : Exploration exploitation in Go: UCT for Monte-Carlo Go. In: Twentieth Annual Conference on Neural Information Processing Systems (2006) 9. : Modification of UCT for Monte-Carlo Go. Technical Report 6062, INRIA (2006) 10. : The Monte-Carlo approach in Amazons.

Springer, Heidelberg (2003) 2. : Monte Carlo Go. Technical report, Physics Department, Syracuse University (1993) 3. : Iterative widening. In: In 17th International Joint Conference on Artificial Intelligence (IJCAI 2001), pp. 523–528 (2001) 4. : Progressive strategies for Monte-Carlo Tree Search. New Mathematics and Natural Computation 4(3), 343–357 (2008) 5. : Efficient selectivity and back-up operators in Monte-Carlo Tree Search. ) CG 2006. LNCS, vol. 4630. Springer, Heidelberg (2007) 6. : Computing “Elo Ratings” of Move Patterns in the Game of Go.

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