Self-organizing neural networks for learning air combat maneuvers

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 STRIVECGF. A self- organizing neural network is used for the adaptive CGF to learn and generalize knowledge in an o...

Full description

Saved in:
Bibliographic Details
Main Authors: TENG, Teck-Hou, TAN, Ah-hwee
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2012
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/6801
https://ink.library.smu.edu.sg/context/sis_research/article/7804/viewcontent/Self_organizing_neural_networks.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-7804
record_format dspace
spelling sg-smu-ink.sis_research-78042022-01-27T08:32:59Z Self-organizing neural networks for learning air combat maneuvers TENG, Teck-Hou TAN, Ah-hwee 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 STRIVECGF. 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. 2012-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6801 info:doi/10.1109/IJCNN.2012.6252763 https://ink.library.smu.edu.sg/context/sis_research/article/7804/viewcontent/Self_organizing_neural_networks.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems OS and Networks
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
OS and Networks
spellingShingle Databases and Information Systems
OS and Networks
TENG, Teck-Hou
TAN, Ah-hwee
Self-organizing neural networks for learning air combat maneuvers
description 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 STRIVECGF. 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.
format text
author TENG, Teck-Hou
TAN, Ah-hwee
author_facet TENG, Teck-Hou
TAN, Ah-hwee
author_sort TENG, Teck-Hou
title Self-organizing neural networks for learning air combat maneuvers
title_short Self-organizing neural networks for learning air combat maneuvers
title_full Self-organizing neural networks for learning air combat maneuvers
title_fullStr Self-organizing neural networks for learning air combat maneuvers
title_full_unstemmed Self-organizing neural networks for learning air combat maneuvers
title_sort self-organizing neural networks for learning air combat maneuvers
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/sis_research/6801
https://ink.library.smu.edu.sg/context/sis_research/article/7804/viewcontent/Self_organizing_neural_networks.pdf
_version_ 1770576071452786688