Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98291
Title: Self-organizing neural networks for learning air combat maneuvers
Authors: Teng, Teck-Hou
Tan, Ah-Hwee
Tan, Yuan-Sin
Yeo, Adrian
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Teng, T. H., Tan, A. H., Tan, Y. S., & Yeo, A. (2012). Self-organizing neural networks for learning air combat maneuvers. The 2012 International Joint Conference on Neural Networks (IJCNN).
Abstract: This paper reports on an agent-oriented approach for the modeling of adaptive doctrine-equipped computer generated force (CGF) using a commercial-grade simulation platform known as CAE STRIVE®CGF. A self-organizing neural network is used for the adaptive CGF to learn and generalize knowledge in an online manner during the simulation. The challenge of defining the state space and action space and the lack of domain knowledge to initialize the adaptive CGF are addressed using the doctrine used to drive the non-adaptive CGF. The doctrine contains a set of specialized knowledge for conducting 1-v-1 dogfights. The hierarchical structure and symbol representation of the propositional rules are incompatible to the self-organizing neural network. Therefore, it has to be flattened and then translated to vector pattern before it can inserted into the self-organizing neural network. The state space and action space are automatically extracted using the flattened doctrine as well. Experiments are conducted using several initial conditions in round robin fashions. The experimental results show that the selforganizing neural network is able to make good use of the domain knowledge with complex knowledge structure to discover the knowledge to out-maneuver the doctrine-driven CGF consistently in an efficient manner.
URI: https://hdl.handle.net/10356/98291
http://hdl.handle.net/10220/12418
DOI: http://dx.doi.org/10.1109/IJCNN.2012.6252763
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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