Multi-agent monte carlo go

10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011

Volumn 1, Issue , 2011, Pages 17-24

  Marcolino, Leandro Soriano ^a   Matsubara, Hitoshi ^a  

^a University Hakodate   (Japan)

Author keywords

Collective intelligence; Emergent behaviour

Indexed keywords

AUTONOMOUS AGENTS; MULTI AGENT SYSTEMS;

COLLECTIVE INTELLIGENCES; COMPUTER GO; EMERGENT BEHAVIORS; EMERGENT BEHAVIOURS; SEARCH SPACES;

MONTE CARLO METHODS;

EID: 84899427863     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. L. V. Allis. Searching for Solutions in Games and Artificial Intelligence. PhD thesis, University of Limburg, Maastricht, The Netherlands, 1994.
2. P. Auer, N. Cesa-Bianchi, and P. Fischer. Finite-time analysis of the multiarmed bandit problem. Mach. Learn., 47:235-256, May 2002.
3. D. B. Benson. Life in the game of go. Inf. Sci., 10(2):17-29, 1976.
4. M. Boon. A pattern matcher for goliath. Computer Go, 13:13-23, 1990.
5. B. Bouzy. Modelisation cognitive du joueur de Go. PhD thesis, Universite Paris, 1995. (in French).
6. B. Bouzy and T. Cazenave. Computer go: An ai oriented survey. Artificial Intelligence, 132:39-103, 2001.
7. B. Brugmann. Monte carlo go. Technical report, Physics Department, Syracuse University, 1993.
8. T. Cazenave. Systeme d'Apprentissage par Auto-Observation. Application au Jeu de Go. PhD thesis, Universite Pierre et Marie Curie, 1996. (in French).
9. T. Cazenave and N. Jouandeau. A parallel monte-carlo tree search algorithm. In CG '08: Proceedings of the 6th international conference on Computers and Games, pages 72-80, Berlin, Heidelberg, 2008. Springer-Verlag.
10. M.-B. Chaslot, M. H. Winands, and H. J. van den Herik. Parallel monte-carlo tree search. In Proceedings of the 6th International Conference on Computer and Games. Springer, 2008.
11. A. Colorni, M. Dorigo, and V. Maniezzo. Distributed optimization by ant colonies. In European Conference on Artificial Life, pages 134-142, 1991.
12. R. Coulom. Efficient selectivity and backup operators in monte-carlo tree search. In H. J. van den Herik, P. Ciancarini, and H. H. L. M. Donkers, editors, Computers and Games, volume 4630 of Lecture Notes in Computer Science, pages 72-83. Springer, 2006.
13. M. Enzenberger and M. Miiller. Fuego - An open-source framework for board games and go engine based on monte-carlo tree search. Technical report, University of Alberta, Dept. of Computing Science, TR09-08, April 2009.
14. M. Enzenberger and M. Miiller. A lock-free multithreaded monte-carlo tree search algorithm. In Advances in Computer Games 12, 2009.
15. K. J. Friedenbach Jr. Abstraction hierarchies: A model of perception and cognition in the game of go. PhD thesis, University of California, Santa Cruz, 1980.
16. S. Gelly, J.-B. Hoock, A. Rimmel, O. Teytaud, and Y. Kalemkarian. On the Parallelization of Monte-Carlo planning. In ICINCO, Madeira Portugal, 2008.
17. S. Gelly, Y. Wang, R. Munos, and O. Teytaud. Modification of UCT with patterns in Monte-Carlo Go. Technical Report 6062, INRIA, France, November 2006.
18. H. Kato and I. Takeuchi. Parallel monte-carlo tree search with simulation servers. In 13th Game Programming Workshop (GPW-08), November 2008.
19. H. Kato and I. Takeuchi. Running "zen" on computer clusters. In 14th Game Programming Workshop (GPW-09), November 2009. (in Japanese).
20. J. Kennedy and R. Eberhart. Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on, volume 4, pages 1942-1948 vol.4, August 2002.
21. L. S. Marcolino and L. Chaimowicz. No robot left behind: Coordination to overcome local minima in swarm navigation. In Proceedings of the 2008 IEEE International Conference on Robotics and Automation, pages 1904-1909, 2008.
22. L. S. Marcolino and L. Chaimowicz. Traffic control for a swarm of robots: Avoiding group conflicts. In Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1949-1954, October 2009.
23. L. S. Marcolino and L. Chaimowicz. Traffic control for a swarm of robots: Avoiding target congestion. In Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1955-1961, October 2009.
24. M. Miiller. Computer Go as a Sum of Local Games: An Application of Combinatorial Game Theory. PhD thesis, Zurich, 1995. (in French).
25. T. Obata, T. Sugiyama, K. Hoki, and T. Ito. Consultation algorithm in shogi: Can a set of players create a single strong player? In 14th Game Programming Workshop (GPW-09), November 2009. (in Japanese).
26. T. Oguri and Y. Kotani. Move decision method based on sds. In 14th Game Programming Workshop (GPW-09), November 2009. (in Japanese).
27. S. E. Page. The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton University Press, January 2007.
28. C. W. Reynolds. Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th annual conference on Computer graphics (SIGGRAPH 87), pages 25-34. ACM Press, 1987.
29. Y. Soejima, A. Kishimoto, and O. Watanabe. Root parallelization of monte carlo tree search and its effectiveness in computer go. In 14th Game Programming Workshop (GPW-09), November 2009. (in Japanese).
30. T. Sugiyama, T. Obata, H. Saito, K. Hoki, and T. Ito. Consultation algorithm in brain game - A move decision based on the positional evaluation value of each player. In 14th Game Programming Workshop (GPW-09), November 2009. (in Japanese).
31. D. Wolfe. Go endgames are pspace-hard. More Games of No Chance, pages 125-136, 2002.
32. S.-J. Yen, C.-W. Chou, S.-C. Hsu, J.-C. Chen, and T.-N. Yang. Improvement of mcts in computer go. In 14th Game Programming Workshop (GPW-09), November 2009.