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Thesis details
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Methods of MCTS and the game Arimaa
Thesis title in Czech: MCTS techniky ve hře Arimaa
Thesis title in English: Methods of MCTS and the game Arimaa
Academic year of topic announcement: 2008/2009
Thesis type: diploma thesis
Thesis language: angličtina
Department: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Supervisor: RNDr. Jan Hric
Author: hidden - assigned and confirmed by the Study Dept.
Date of registration: 27.11.2008
Date of assignment: 03.12.2008
Date and time of defence: 21.09.2009 00:00
Date of electronic submission:21.09.2009
Date of proceeded defence: 21.09.2009
Opponents: Mgr. Vladan Majerech, Dr.
 
 
 
Guidelines
A student exploites possibilities of the MCTS method (Monte Carlo + UCT algorithm [1]) in the strategic board game Arimaa [2]. He suggests and implements computer player based on this techniques.

He investigates possibilities of integration a static evaluation function into Monte Carlo simulations. He tries variants and extensions of the MCTS method described in literature (e.g. for the game Go [3],[4],[5],[6]) or his own and adapts them for the game Arimaa.
References
[1] Levente Kocsis and Csaba Szepesvári: Bandit Based Monte-Carlo Planning. In: European Conference on Machine learning, ECML-06, LNCS 4212, Springer 2006, pp. 282-293
[2] Omar Syed: Arimaa: A New Game Designed to be Difficult for Computers. ICGA Journal Vol. 26, No.2, pp.138-139.
[3] Sylvain Gelly and Yizao Wang. Exploration Exploitation in Go: UCT for Monte-Carlo Go, On-line Trading of Exploration and Exploitation, NIPS 2006 Workshop, Whistler, Canada, 2006
[4] Sylvain Gelly, Yizao Wang, Rémi Munos, and Olivier Teytaud: Modification of UCT with Patterns in Monte-Carlo Go, TR 6062, INRIA, November 2006
[5] Guillaume Chaslot, Mark H. M. Winands, H. Jaap van den Herik, Jos Uiterwijk, and Bruno Bouzy. Progressive Strategies for Monte-Carlo Tree Search. In: Joint Conference on Information Sciences, Salt Lake City 2007, 10th International Conference on Computer Science & Informatics (CSI), 2007, pp. 655-661
[6] Sylvain Gelly, David Silver: Combining Online and Offline Knowledge in UCT. International Conference on Machine Learning (ICML), ACM, 2007, pp.273-280

Preliminary scope of work
Metody MCTS a hra Arimaa.

Prozkoumejte možnosti uplatnění MCTS metod (Monte Carlo + UCT algoritmus [1]) ve strategické
deskové hře Arimaa [2], navrhněte a implementujte počítačového hráče postaveného na těchto technikách.

Vyšetřete možnosti integrace statické ohodnocovací funkce do Monte Carlo simulací. Vyzkoušejte varianty a rozšíření MCTS (popsané v literatuře např. pro hru go [3],[4],[5],[6]) nebo vlastní a upravte je pro hru Arimaa.
 
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