tutorial

Adaptive Human Behavior Modeling for Air Combat Simulation

Authors:

Jian Yao,

Qiwang Huang,

Weiping WangAuthors Info & Claims

DS-RT 2015: Proceedings of the 19th International Symposium on Distributed Simulation and Real Time Applications

Pages 100 - 103

https://doi.org/10.1109/DS-RT.2015.12

Published: 14 October 2015 Publication History

Get Access

Abstract

Military simulations, especially those for personnel training and equipment effectiveness analysis, require proper human behavior models (HBMs) to play blue or red. Traditionally, the HBMs are controlled through rule based scripts. However, the doctrine-driven behavior is rigid and predictable, and more often than not unable to adapt to new situations. In most cases, the subject matter experts (SMEs) review, re-design a large amount of HBM scripts for new scenarios or training tasks, which is challenging and time-consuming. Therefore, a study of using Grammatical Evolution (GE) to generate adaptive HBMs for air combat simulation is conducted in this work. Expert knowledge is encoded with modular behavior trees (BTs) for the compatibility with the operators in genetic algorithm (GA). GE maps HBMs represented with BTs to binary strings, and uses GA to evolve HBMs with the performance fed back from simulation. Beyond visual range air combat experiments between adaptive HBMs and none-adaptive baseline HBMs are conducted to study the evolutionary process. The experimental results show that the GE is an efficient framework to generate adaptive HBMs in BTs formalism and evolve them with GA.

References

[1]

S. Mittal, M. J. Doyle, and E. Watz, "Detecting intelligent agent behavior with environment abstraction in complex air combat systems," in International Systems Conference (SysCon). IEEE, 2013, pp. 662--670.

Google Scholar

[2]

NATO, "Human behavior representation in constructive simulation," NATO RTO, Tech. Rep. RTO-TR-HFM-128, 2009.

Google Scholar

[3]

M. O'Neill and C. Ryan, Grammatical Evolution. Springer US, 2003.

Crossref

Google Scholar

[4]

M. Colledanchise and P. Ögren, "How behavior trees modularize robustness and safety in hybrid systems," in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA, September 14-18, 2014, pp. 1482--1488.

Google Scholar

[5]

M. Mitchell, An introduction to genetic algorithms. MIT press, 1998.

Digital Library

Google Scholar

[6]

J. E. Laird, K. J. Coulter, O. M. Jones, P. G. Kenny, F. Koss, and P. E. Nielsen, "Integrating intelligent computer generated forces in distributed simulation: Tacair-soar," in Proceedings of the 1998 Simulation Interoperability Workshop in STOW-97, 1998.

Google Scholar

[7]

A. Toubman, J. J. Roessingh, P. Spronck, A. Plaat, and H. J. van den Herik, "Dynamic scripting with team coordination in air combat simulation," in Modern Advances in Applied Intelligence - 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Kaohsiung, Taiwan, 2014, pp. 440--449.

Digital Library

Google Scholar

[8]

T.-H. Teng, A.-H. Tan, Y.-S. Tan, and A. Yeo, "Self-organizing neural networks for learning air combat maneuvers," in The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE, 2012, pp. 1--8.

Google Scholar

[9]

J. Roessingh, R. Rijken, R. Merk, R. Meiland, P. Huibers, T. Lue, and C. Montijn, "Modelling cgfs for tactical air-to-air combat training motivation-based behaviour and machine learning in a common architecture," National Aerospace Laboratory NLR, Tech. Rep. NLR-TP-2011-540, 2011.

Google Scholar

[10]

A. Champandard, "Behavior trees for next-gen game ai," in Game Developers Conference, Audio Lecture, 2007.

Google Scholar

[11]

R. L. Shaw, Fighter combat: Tactics and maneuvering. Naval Institute Press, 1985.

Google Scholar

[12]

Y.-l. LEI, Q. LI, F. YANG, W.-p. WANG, and Y.-f. ZHU, "A composable modeling framework for weapon systems effectiveness simulation," Systems Engineering Theory & Practice, vol. 33, no. 11, pp. 2954--2966, 2013.

Google Scholar

Cited By

View all

Iovino MScukins EStyrud JÖgren PSmith C(2022)A survey of Behavior Trees in robotics and AIRobotics and Autonomous Systems10.1016/j.robot.2022.104096154:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.robot.2022.104096
Källström JEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Adaptive Agent-Based Simulation for Individualized TrainingProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3399122(2193-2195)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3399122
Toubman ARoessingh JSpronck PPlaat Avan den Herik J(2016)Rapid adaptation of air combat behaviourProceedings of the Twenty-second European Conference on Artificial Intelligence10.3233/978-1-61499-672-9-1791(1791-1796)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.3233/978-1-61499-672-9-1791

Recommendations

Infrared Air Combat Simulation Model for Deep Reinforcement Learning
CSAE '21: Proceedings of the 5th International Conference on Computer Science and Application Engineering

Aiming at the problem of lacking credible and realistic infrared air combat simulation platform for applying the deep reinforcement learning method, this paper explored the design requirements for the construction of simulation system, built the overall ...
A Tactical Planning Process in Computer-Generated Forces Team Behavior Within Air Combat Simulations: Concept and First Implementations
Modelling and Simulation for Autonomous Systems
Abstract
Due to a rising number of entities and more advanced systems, modern air combat engagements are increasing in complexity. Therefore, to ensure success in pilot training and perform accurate threat evaluation using simulations, it is substantial to ...
Army Men Air Combat

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

DS-RT 2015: Proceedings of the 19th International Symposium on Distributed Simulation and Real Time Applications

October 2015

224 pages

ISBN:9781467378222

Publisher

IEEE Press

Publication History

Published: 14 October 2015

Check for updates

Author Tags

Qualifiers

Tutorial
Research
Refereed limited

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
74
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)1

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Iovino MScukins EStyrud JÖgren PSmith C(2022)A survey of Behavior Trees in robotics and AIRobotics and Autonomous Systems10.1016/j.robot.2022.104096154:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.robot.2022.104096
Källström JEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Adaptive Agent-Based Simulation for Individualized TrainingProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3399122(2193-2195)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3399122
Toubman ARoessingh JSpronck PPlaat Avan den Herik J(2016)Rapid adaptation of air combat behaviourProceedings of the Twenty-second European Conference on Artificial Intelligence10.3233/978-1-61499-672-9-1791(1791-1796)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.3233/978-1-61499-672-9-1791

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

Infrared Air Combat Simulation Model for Deep Reinforcement Learning

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

Army Men Air Combat