A neural-network based behavioral theory of tank commanders
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Authors
Bui, Tung X.
Dryer, David A.
Laskowski, Matthew Ludwig
Subjects
Combat theory, Knowledge exploration, Inductive reasoning, Applied Artificial Intelligence
Advisors
Date of Issue
1992-05
Date
1992-05
Publisher
Monterey, California. Naval Postgraduate School
Language
eng
Abstract
Based on the presumption that data observed in high-tech and fast changing battles do have some
intrinsic richness to them that synthetic modelling fails to capture, we contend that data induction
techniques can be successfully used to generalize combat behaviors. This paper reports the use of
neural networks as a computer-based adaptive induction algorithm to understand and uncover ground
combat behaviors. Experiments with neural networks using tank movement data from the National
Training Center (NTC), Fort Irwin, demonstrate that a two-dimensional cognitive map of closely task
organized units can be derived. The findings seem to confirm our behavioral theory that tank
commanders (i) are mission-driven, (if) act as an integral part of their platoon, (iii) perform sequential
decision making to determine their next moves, and (iv) when isolated, extemporaneous behaviors may
take precedence over normative gorup behavior. Once trained, a neural-network based model of
closely task organized units can be used to predict the itinerary sequences of a tank given its initial geographic position.
Type
Technical Report
Description
Series/Report No
Department
Academic Sciences
Identifiers
NPS Report Number
NPS-AS-92-015
Sponsors
U.S. Army Training and Doctrine Command Monterey, CA
Funder
N6227192MPR0014
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.