Effective team strategies using dynamic scripting

作者: Robert G. Price , Scott D. Goodwin

DOI:

关键词:

摘要: Forming effective team strategies using heterogeneous agents to accomplish a task can be challenging problem. The number of combinations actions look through enormous, and having an agent that is really good at particular sub-task no guarantee will perform well on with members different abilities. Dynamic Scripting has been shown way improving behaviours adaptive game AI. We present approach modifies the scripting process account for other in game. By analyzing agent's allies opponents we create better starting scripts use. Creating points minimize iterations needed learn strategies, creating overall gaming experience.

参考文章(79)
Michael J. Smith, Marie desJardins, Learning to trust in the competence and commitment of agents Autonomous Agents and Multi-Agent Systems. ,vol. 18, pp. 36- 82 ,(2009) , 10.1007/S10458-008-9055-8
Kenneth O. Stanley and Bobby D. Bryant and Risto Miikkulainen, Evolving Neural Network Agents in the NERO Video Game ,(2005)
E.G. Jones, B. Browning, M.B. Dias, B. Argall, M. Veloso, A. Stentz, Dynamically formed heterogeneous robot teams performing tightly-coordinated tasks international conference on robotics and automation. pp. 570- 575 ,(2006) , 10.1109/ROBOT.2006.1641771
Anthony Stentz, M. Bernardine Dias, Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments Carnegie Mellon University. ,(2004)
M. Tambe, D.V. Pynadath, N. Chauvat, A. Das, G.A. Kaminka, Adaptive agent integration architectures for heterogeneous team members Proceedings Fourth International Conference on MultiAgent Systems. pp. 301- 308 ,(2000) , 10.1109/ICMAS.2000.858467
I. Szita, M. Ponsen, P. Spronck, Effective and Diverse Adaptive Game AI IEEE Transactions on Computational Intelligence and AI in Games. ,vol. 1, pp. 16- 27 ,(2009) , 10.1109/TCIAIG.2009.2018706
Sven Koenig, A Comparison of Fast Search Methods for Real-Time Situated Agents adaptive agents and multi-agents systems. pp. 864- 871 ,(2004) , 10.1109/AAMAS.2004.7
John E. Laird, Michael van Lent, Human-Level AI's Killer Application: Interactive Computer Games national conference on artificial intelligence. pp. 1171- 1178 ,(2000)
Duane Szafron, Richard Zhao, AmirAli Sharifi, Learning companion behaviors using reinforcement learning in games national conference on artificial intelligence. pp. 69- 75 ,(2010)