RETALIATE: Learning winning policies in first-person shooter games

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2007, Pages 1801-1806

  Smith, Megan ^a   Lee Urban, Stephen ^a   Muñoz Avila, Héctor ^a  

^a Lehigh University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMATION; LEARNING ALGORITHMS; ONLINE SYSTEMS; PROBLEM SOLVING; THREE DIMENSIONAL COMPUTER GRAPHICS;

EMPIRICAL EVALUATION; STATE FORMULATION;

REINFORCEMENT LEARNING;

EID: 36348963576     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Bowling, M. & Veloso, M. Multiagent learning using a variable learning rate. Artificial Intelligence, vol. 136, no. 2, pp. 215-250, 2002.
2. Fu & Houlette. Constructing a Decision Tree Based on Past Experience. AI Game Programming Wisdom 2. Charles River Media, 2003.
3. Gamebot. http://www.planetunreal.com/gamebots/. Last viewed: January 24, 2005.
4. Hoang, H., Lee-Urban, S., and Muñoz-Avila, H. Hierarchical Plan Representations for Encoding Strategic Game AI. Proceedings of Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE-05). AAAI Press. 2005
5. Kent, T. Multi-Tiered AI Layers and Terrain Analysis for RTS Games. In: Al Game Programming Wisdom 2. Charles River Media, 2003.
6. Laird, J.E., & van Lent, M. Interactive computer games: Human-level AI's killer application. AI Magazine, 22(2), 15-25. 2001
7. Lauer, M. & Riedmiller, M. An algorithm for distributed reinforcement learning in cooperative multi-agent systems. In Proceedings of the Seventeenth International Conference on Machine Learning, pp 535-542, 2000.
8. Matsubara, H., Noda, I., & Hiraki, K. Learning of cooperative actions in multiagent systems: a case study of pass play in soccer. In Adaptation, Coevolution and Learning in Multiagent Systems: AAAI Spring Symposium, pgs. 63-67, 1996.
9. Muñoz-Avila, H. & Hoang, H. (2006) Coordinating Teams of Bots with Hierarchical Task Network Planning. In: AI Game Programing Wisdom 3. Charles River Media.
10. Nau, D., Cao, Y., Lotem, A., & Muñoz-Avila, H. SHOP: Simple hierarchical ordered planner. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99). Stockholm: AAAI Press, 1999.
11. Ponsen, M. and Spronck, P. (2004). Improving Adaptive Game AI with Evolutionary Learning. Computer Games: Artificial Intelligence, Design and Education (CGAIDE 2004). pp. 389-396. University of Wolverhampton.
12. Rabin, S. Promising Game AI Techniques. In: AI Game Programming Wisdom 2. Charles River Media, 2003.
13. Reynolds, J. Team Member AI in an FPS. In: AI Game Programming Wisdom 2. Charles River Media, 2003.
14. Sen, S., Mahendra, S., Hale, J. Learning to Coordinate Without Sharing Information. Proceedings of the Twelfth National Conference on Artificial Intelligence, pp. 426-431. 1994
15. Spronck, P., Sprinkhuizen-Kuyper, I. and Postma. E. 2004. Online Adaptation of Game Opponent AI with Dynamic Scripting. International Journal of Intelligent Games and Simulation, Vol. 3, No. 1, University of Wolverhampton and EUROSIS, pp. 45-53.
16. Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998.
17. Nair, R. & Tambe, M. Journal of AI Research (JAIR), 23:367-420, 2005.
18. Tesauro, G. Temporal Difference Learning and TD-Gammon. In Communications of the ACM, March 1995 / Vol. 38, No. 3, 1995.
19. van Lent, M., Laird, J. E., Buckman, J., Hartford, J., Houchard, S., Steinkraus, K. and Tedrake, R., Intelligent Agents in Computer Games. Proceedings of the National Conference on Artificial Intelligence, pp. 929-930, 1999.
20. Watkins, C. J. Models of Delayed Reinforcement Learning. Ph.D. thesis, Psychology Department, Cambridge University, Cambridge, UK, 1989.
21. Yiskis, E. A Subsumption Architecture for Character-Based Games. In: AI Game Programming Wisdom 2. Charles River Media, 2003.