Article

Dynamic Scripting with Team Coordination in Air Combat Simulation

Authors:

Jaap HerikAuthors Info & Claims

IEA/AIE 2014: Proceedings, Part I, of the 27th International Conference on Modern Advances in Applied Intelligence - Volume 8481

Pages 440 - 449

https://doi.org/10.1007/978-3-319-07455-9_46

Published: 03 June 2014 Publication History

Abstract

Traditionally, behavior of Computer Generated Forces CGFs is controlled through scripts. Building such scripts requires time and expertise, and becomes harder as the domain becomes richer and more life-like. These downsides can be reduced by automatically generating behavior for CGFs using machine learning techniques. This paper focuses on Dynamic Scripting DS, a technique tailored to generating agent behavior. DS searches for an optimal combination of rules from a rule base. Under the assumption that intra-team coordination leads to more effective learning, we propose an extension of DS, called DS+C, with explicit coordination. In a comparison with regular DS we find that the addition of team coordination results in earlier convergence to optimal behavior. In addition, we achieved a performance increase of 20% against an unpredictable opponent. With DS+C, behavior for CGFs can be generated that is more effective since the CGFs act on knowledge achieved by coordination and the behavior converges more efficiently than with regular DS.

References

[1]

Laird, J.E.: An exploration into computer games and computer generated forces. In: Eighth Conference on Computer Generated Forces and Behavior Representation 2000

Google Scholar

[2]

Fletcher, J.D.: Education and training technology in the military. Science 323, 72---75 2009

Crossref

Google Scholar

[3]

Roessingh, J.J., Merk, R.-J., Huibers, P., Meiland, R., Rijken, R.: Smart Bandits in air-to-air combat training: Combining different behavioural models in a common architecture. In: 21st Annual Conference on Behavior Representation in Modeling and Simulation, Amelia Island, Florida, USA 2012

Google Scholar

[4]

Benjamin, P., Graul, M., Akella, K.: Towards Adaptive Scenario Management ASM. In: The Interservice/Industry Training, Simulation & Education Conference I/ITSEC, pp. 1478---1487. National Training Systems Association 2012

Google Scholar

[5]

De Kraker, K.J., Kerbusch, P., Borgers, E.: Re-usable behavior specifications for tactical doctrine. In: Proceedings of the 18th Conference on Behavior Representation in Modeling and Simulation BRIMS 2009, Sundance, Utah, USA, pp. 15---22 2009

Google Scholar

[6]

Koopmanschap, R., Hoogendoorn, M., Roessingh, J.J.: Learning Parameters for a Cognitive Model on Situation Awareness. In: The 26th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, Amsterdam, the Netherlands, pp. 22---32 2013

Digital Library

Google Scholar

[7]

Spronck, P., Ponsen, M., Sprinkhuizen-Kuyper, I., Postma, E.: Adaptive game AI with dynamic scripting. Mach. Learn. 63, 217---248 2006

Digital Library

Google Scholar

[8]

Van der Sterren, W.: Squad Tactics: Team AI and Emergent Maneuvers. In: Rabin, S. ed. AI Game Programming Wisdom, pp. 233---246. Charles River Media, Inc. 2002

Google Scholar

[9]

Stone, P., Veloso, M.: Multiagent systems: A survey from a machine learning perspective. Auton. Robots. 8, 345---383 2000

Digital Library

Google Scholar

[10]

Balch, T., Arkin, R.C.: Communication in reactive multiagent robotic systems. Auton. Robots 1, 27---52 1994

Digital Library

Google Scholar

[11]

Szita, I., Ponsen, M., Spronck, P.: Effective and Diverse Adaptive Game AI. IEEE Trans. Comput. Intell. AI Games 1, 16---27 2009

Crossref

Google Scholar

Cited By

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Huang HTong ZChai SWang CWang BLi S(2018)Study of IR Defense Strategy Based on IR Guided MissileProceedings of the International Conference on Information Technology and Electrical Engineering 201810.1145/3148453.3306303(1-5)Online publication date: 7-Dec-2018
https://dl.acm.org/doi/10.1145/3148453.3306303
Yao JHuang QWang W(2015)Adaptive Human Behavior Modeling for Air Combat SimulationProceedings of the 19th International Symposium on Distributed Simulation and Real Time Applications10.1109/DS-RT.2015.12(100-103)Online publication date: 14-Oct-2015
https://dl.acm.org/doi/10.1109/DS-RT.2015.12
Kop RToubman AHoogendoorn MRoessingh J(2015)Evolutionary Dynamic ScriptingProceedings of the 28th International Conference on Current Approaches in Applied Artificial Intelligence - Volume 910110.1007/978-3-319-19066-2_6(53-62)Online publication date: 10-Jun-2015
https://dl.acm.org/doi/10.1007/978-3-319-19066-2_6

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Published In

IEA/AIE 2014: Proceedings, Part I, of the 27th International Conference on Modern Advances in Applied Intelligence - Volume 8481

June 2014

520 pages

ISBN:9783319074542

Editors:
Moonis Ali
Department of Computer Science, Texas State University, San Marcos, USA
,
Jeng-Shyang Pan
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
,
Shyi-Ming Chen
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
,
Mong-Fong Horng
Department of Electronics Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan ROC

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Springer-Verlag

Berlin, Heidelberg

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Published: 03 June 2014

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Huang HTong ZChai SWang CWang BLi S(2018)Study of IR Defense Strategy Based on IR Guided MissileProceedings of the International Conference on Information Technology and Electrical Engineering 201810.1145/3148453.3306303(1-5)Online publication date: 7-Dec-2018
https://dl.acm.org/doi/10.1145/3148453.3306303
Yao JHuang QWang W(2015)Adaptive Human Behavior Modeling for Air Combat SimulationProceedings of the 19th International Symposium on Distributed Simulation and Real Time Applications10.1109/DS-RT.2015.12(100-103)Online publication date: 14-Oct-2015
https://dl.acm.org/doi/10.1109/DS-RT.2015.12
Kop RToubman AHoogendoorn MRoessingh J(2015)Evolutionary Dynamic ScriptingProceedings of the 28th International Conference on Current Approaches in Applied Artificial Intelligence - Volume 910110.1007/978-3-319-19066-2_6(53-62)Online publication date: 10-Jun-2015
https://dl.acm.org/doi/10.1007/978-3-319-19066-2_6

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A Situation Analysis Process in Computer-Generated Forces Team Behavior Within Air Combat Simulations Under Risk and Uncertainty: Concept and First Implementations

A Tactical Planning Process in Computer-Generated Forces Team Behavior Within Air Combat Simulations: Concept and First Implementations

Advancing Air Combat Tactics with Improved Neural Fictitious Self-play Reinforcement Learning

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