Crowd simulation via multi-Agent reinforcement learning

Proceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010

Volumn , Issue , 2010, Pages 89-94

  Torrey, Lisa ^a  

^a ST LAWRENCE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CROWD SIMULATION; LEARNING TECHNIQUES; MULTI-AGENT REINFORCEMENT LEARNING; VIRTUAL CHARACTER;

HUMAN COMPUTER INTERACTION;

ARTIFICIAL INTELLIGENCE;

EID: 84883109899     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Bowling, M., and Veloso, M. 2001. Rational and convergent learning in stochastic games. In The International Joint Conference on Artificial Intelligence (IJCAI'01).
2. Busoniu, L.; Babuska, R.; and Schutter, B. D. 2008. A comprehensive survey of multi-Agent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics 38(2):156-172.
3. Conitzer, V., and Sandholm, T. 2006. AWESOME: A general multiagent learning algorithm that converges in selfplay and learns a best response against stationary opponents. In The International Conference on Machine Learning (ICML'06).
4. Dietterich, T. 2000. State abstraction inMAXQ hierarchical reinforcement learning. In Advances in Neural Information Processing Systems (NIPS'00).
5. Funge, J.; Tu, X.; and Terzopoulos, D. 1999. Cognitive modeling: knowledge, reasoning and planning for intelligent characters. In The International Conference and Exhibition on Computer Graphics and Interactive Techniques (SIGGRAPH'99).
6. Heigeas, L.; Luciani, A.; Thollot, J.; and Castagne, N. 2003. A physically-based particle model of emergent crowd behaviors. In The International Conference on Computer Graphics and Vision (GRAPHICON'03).
7. Johnson, D., andWiles, J. 2001. Computer gameswith intelligence. In The Australian Journal of Intelligent Information Processing Systems.
8. Lerner, A.; Chrysanthou, Y.; and Lischinski, D. 2007. Crowds by example. In The Conference of the European Association for Computer Graphics (EUROGRAPH'07).
9. Leslie, D., and Collins, E. 2002. Multiple-Timescale Qlearning. In The NIPS Workshop on Multi-Agent Learning.
10. Miles, J., and Tashakkori, R. 2010. Improving believability of simulated characters. Journal of Computing Science in Colleges 25(3):32-39.
11. Sonar, A.; Burdick, K.; Begin, R.; Resch, E.; Thompson, E.; Thacher, E.; Searleman, J.; Fulk, G.; and Carroll, J. 2005.Development of a virtual reality-based power wheel chair simulator. In The International Conference onMechatronics and Automation.
12. Spronck, P.; Ponsen, M.; Sprinkhuizen-Kuyper, I.; and Postma, E. 2006. Adaptive game AI with dynamic scripting. Machine Learning 63(3):217-248.
13. Stone, P., and Veloso, M. 2000. Multiagent systems: A survey from a machine learning perspective. Autonomous Robots 8(3):345-383.
14. Sung, M.; Gleicher, M.; and Chenney, S. 2004. Scalable behaviors for crowd simulation. Computer Graphics Forum 23(3):519-528.
15. Sutton, R., and Barto, A. 1998. Reinforcement Learning: An Introduction. Cambridge, Massachusetts: MIT Press.
16. Taylor, M., and Stone, P. 2009. Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research 10(1):1633-1685.
17. Tesauro, G., and Kephart, J. 2002. Pricing in agent economies using multi-Agent Q-learning. In The International Conference on Autonomous Agents and Multiagent Systems (AAMAS'02).
18. Torrey, L. 2009. Relational transfer in reinforcement learning. Ph.D. Dissertation, University of Wisconsin.