Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)

A Learning Behavioral Model of CGF

Authors
Xianquan Meng1, Yingnan Zhao, Liguo Wang, Qing Xue
1Simulation Center, Department of Equipment Command & Management, Academy of Armored Force Engineering
Corresponding Author
Xianquan Meng
Available Online October 2007.
DOI
https://doi.org/10.2991/iske.2007.175How to use a DOI?
Keywords
Genetic algorithms, CGF, Agent, LCSs, learning behavioral modeling
Abstract

The Computer Generated Force (CGF) has decision ability by self-learning mechanism, which is an important research field in applying machine learning technology to military simulation. On the basis of modeling architecture of Agent and Learning Classifier Systems (LCSs) technologies, a learning behavioral model framework based on Genetic Algorithms (GA) of CGF is proposed. It discusses elaborately the learning process of this model, in which memory function is first introduced to accelerate. Also, a visible validation system is designed. The simulated results indicate that the learning model is available and feasible

Copyright
© 2007, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
Series
Advances in Intelligent Systems Research
Publication Date
October 2007
ISBN
978-90-78677-04-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/iske.2007.175How to use a DOI?
Copyright
© 2007, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xianquan Meng
AU  - Yingnan Zhao
AU  - Liguo Wang
AU  - Qing Xue
PY  - 2007/10
DA  - 2007/10
TI  - A Learning Behavioral Model of CGF
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 1029
EP  - 1034
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.175
DO  - https://doi.org/10.2991/iske.2007.175
ID  - Meng2007/10
ER  -