Non-linear Monte-Carlo search in civilization II

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2011, Pages 2404-2410

  Branavan, S R K ^a   Silver, David ^b   Barzilay, Regina ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION-MAKING PROBLEM; DOMAIN KNOWLEDGE; EXTERNAL SOURCES; LINGUISTIC KNOWLEDGE; MONTE-CARLO TREE SEARCHES; MULTI-AGENT STRATEGY; NON-LINEAR REGRESSION; SEARCH ALGORITHMS;

ALGORITHMS; MULTI AGENT SYSTEMS;

KNOWLEDGE MANAGEMENT;

EID: 84881054071     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5591/978-1-57735-516-8/IJCAI11-401     Document Type: Conference Paper

References (19)

1. Christopher Amato and Guy Shani. High-level reinforcement learning in strategy games. In Proceedings of AAMAS, pages 75-82, 2010.
2. R. Balla and A. Fern. UCT for tactical assault planning in real-time strategy games. In 21st International Joint Conference on Artificial Intelligence, 2009.
3. Maurice Bergsma and Pieter Spronck. Adaptive spatial reasoning for turn-based strategy games. In Proceedings of AIIDE, 2008.
4. Darse Billings, Lourdes Peña Castillo, Jonathan Schaeffer, and Duane Szafron. Using probabilistic knowledge and simulation to play poker. In 16th National Conference on Artificial Intelligence, pages 697-703, 1999.
5. S.R.K Branavan, David Silver, and Regina Barzilay. Learning to win by reading manuals in a monte-carlo framework. In Proceedings of ACL, 2011.
6. John S. Bridle. Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters. In Advances in NIPS, pages 211-217, 1990.
7. R. Coulom. Efficient selectivity and backup operators in Monte-Carlo tree search. In 5th International Conference on Computer and Games, pages 72-83, 2006.
8. Marie-Catherine de Marneffe, Bill Mac-Cartney, and Christopher D. Manning. Generating typed dependency parses from phrase structure parses. In LREC 2006, 2006.
9. S. Gelly and D. Silver. Combining online and offline learning in UCT. In 17th International Conference on Machine Learning, pages 273-280, 2007.
10. S. Gelly, Y. Wang, R. Munos, and O. Teytaud. Modification of UCT with patterns in Monte-Carlo Go. Technical Report 6062, INRIA, 2006.
11. Charles Madeira, Vincent Corruble, and Geber Ramalho. Designing a reinforcement learning-based adaptive ai for large-scale strategy games. In Proceedings of AIIDE, pages 121-123, 2006.
12. J. Schäfer. The UCT algorithm applied to games with imperfect information. Diploma Thesis. Otto-von-Guericke-Universität Magdeburg, 2008.
13. B. Sheppard. World-championship-caliber Scrabble. Artificial Intelligence, 134(1-2):241-275, 2002. (Pubitemid 34086585)
14. D. Silver, R. Sutton, and M. Müller. Samplebased learning and search with permanent and transient memories. In 25th International Conference on Machine Learning, pages 968-975, 2008.
15. N. Sturtevant. An analysis of UCT in multiplayer games. In 6th International Conference on Computers and Games, pages 37-49, 2008.
16. Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, 1998.
17. G. Tesauro and G. Galperin. On-line policy improvement using Monte-Carlo search. In Advances in Neural Information Processing 9, pages 1068-1074, 1996.
18. G. Tesauro. TD-gammon, a self-teaching backgammon program, achieves master-level play. Neural Computation, 6:215-219, 1994.
19. Stefan Wender and Ian Watson. Using reinforcement learning for city site selection in the turn-based strategy game civilization iv. In Proceedings of CIG, pages 372-377, 2008.