Abstract
Traditional behavior modeling methods rely on the knowledge representation derived from the induction and abstraction of subject matter experts, leading to the high barrier and long modeling period. To tackle this problem, we focus on a new behavior modeling approach, which extracts behavior knowledge from behavior data using recurrent neural network (RNN). A case study, take-off behavior modeling using long short-term memory (LSTM) network, was carried out in three phases: the data recording phase, the offline model training phase and the online model execution phase. A three-layer neural network was constructed to learn the pattern of take-off manipulations. The resulting take-off behavior model performed well to ‘pilot’ an airplane in the real-time test.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Rotation speed is the speed at which the pilot begins to apply control inputs to cause the aircraft nose to pitch up, after which it will leave the ground [17].
References
NATO Research and Technology Organisation: Human behavior representation in constructive simulation. Technical report RTO-TR-HFM-128 (2009)
Tian, S.C., Sun, F., Li, Z.Y., et al.: Research on CGF behavior modeling based on rule. Appl. Mech. Mater. 347, 3056–3059 (2013)
Xiu-Luo, L., Huang, K., Xiao-Jun, Z.: The application of finite state machine in the CGF’s behavior modeling. Acta Simulata Systematica Sinica 5, 037 (2001)
Laird, J.E., Newell, A., Rosenbloom, P.S.: Soar: an architecture for general intelligence. Artif. Intell. 33(1), 1–64 (1987)
Anderson, J.R., Matessa, M., Lebiere, C.: ACT-R: a theory of higher level cognition and its relation to visual attention. Hum.-Comput. Interact. 12(4), 439–462 (1997)
Lipton, Z.C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Ghosh, S., Vinyals, O., Strope, B., et al.: Contextual LSTM (CLSTM) models for large scale NLP tasks. arXiv preprint arXiv:1602.06291 (2016)
Luan, Y., Ji, Y., Ostendorf, M.: LSTM based conversation models. arXiv preprint arXiv:1603.09457 (2016)
Greff, K., Srivastava, R.K., Koutník, J., et al.: LSTM: a search space odyssey. arXiv preprint arXiv:1503.04069 (2015)
Johnson, R., Zhang, T.: Supervised and semi-supervised text categorization using one-hot LSTM for region embeddings. arXiv preprint arXiv:1602.02373 (2016)
Zhu, W., Lan, C., Xing, J., et al.: Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. In: The 30th AAAI Conference on Artificial Intelligence (2015)
Lipton, Z.C., Kale, D.C., Elkan, C., et al.: Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677 (2015)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Christopher, O.: Understanding LSTM Networks (2015). http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Keras development team. Keras documentation, usage of optimizer (2016). http://keras.io/optimizers/
Wikipedia. V speeds (2016). https://en.wikipedia.org/wiki/V_speeds/
Perry, A.R.: The flightgear flight simulator. In: Proceedings of the USENIX Annual Technical Conference (2004)
FlightGear development team. Introduction to FlightGear (2016). http://www.flightgear.org/about/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Zhou, Y., Fu, Z., Gong, G. (2016). Pilot Behavior Modeling Using LSTM Network: A Case Study. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-10-2666-9_46
Download citation
DOI: https://doi.org/10.1007/978-981-10-2666-9_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2665-2
Online ISBN: 978-981-10-2666-9
eBook Packages: Computer ScienceComputer Science (R0)