Monte carlo planning in RTS games

IEEE 2005 Symposium on Computational Intelligence and Games, CIG'05

Volumn , Issue , 2005, Pages 117-124

  Chung, Michael ^a   Buro, Michael ^a   Schaeffer, Jonathan ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

IMPERFECT INFORMATION; MONTE CARLO; MONTE CARLO SIMULATION; PERFORMANCE PARAMETERS; PLANNING IN GAMES; REAL-TIME STRATEGY GAMES; STOCHASTICITY;

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION;

MONTE CARLO METHODS;

EID: 80053628578     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. http://www.blizzard.com.
2. http://www.ensemblestudios.com.
3. http://www.ict.usc.edu/disp.php?bd=proj-games-fsc1.
4. http://www.cs.ualberta.ca/~mburo/orts.
5. D. Billings, L. Pena, J. Schaeffer, and D. Szafron. Using probabilistic knowledge and simulation to play poker. In AAAI National Conference, pages 697-703, 1999.
6. B. Bouzy and B. Helmstetter. Monte Carlo go developments. In Advances in Computer Games X, pages 159-174. Kluwer Academic Press, 2003.
7. M. Buro and T. Furtak. RTS games and real-time AI research. In Proceedings of the Behavior Representation in Modeling and Simulation Conference (BRIMS), Arlington VA 2004, pages 51-58, 2004.
8. I. Frank and D.A. Basin. Search in games with incomplete information: A case study using bridge card play. AI Journal, 100(1-2):87- 123, 1998. (Pubitemid 128403238)
9. M. Ginsberg. GIB: Steps toward an expert-level bridge-playing program. In International Joint Conference on Artificial Intelligence, pages 584-589, 1999.
10. Jonathan Schaeffer, Darse Billings, Lourdes Peña, and Duane Szafron. Learning to Play Strong Poker. In J. Fürnkranz and M. Kubat, editors, Machines That Learn To Play Games, pages 225-242. Nova Science Publishers, 2001.
11. B. Sheppard. Towards Perfect Play in Scrabble. PhD thesis, 2002.
12. B. Sheppard. Efficient control of selective simulations. Journal of the international Computer Games Association, 27(2):67-80, 2004.
13. R. Sutton and A. Barto. Reinforcement Learning. MIT Press, 1998.
14. G. Tesauro. Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3):58-68, 1995.