PREDICTING OPPONENT POSITION AND MODELING UNCERTAINTY
Loading...
Authors
Maroon, Kenneth J.
Subjects
modeling and simulation
automated planning
combat modeling
prediction modeling
risk assessment
artificial intelligence
AI
formation evaluation
automated planning
combat modeling
prediction modeling
risk assessment
artificial intelligence
AI
formation evaluation
Advisors
Buss, Arnold H.
Appleget, Jeffrey A.
Darken, Christian J.
Alt, Jonathan K.
Balogh, Imre L.
Date of Issue
2020-09
Date
Sep-20
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Current combat simulation software developments for automated planning do not account for fog-of-war in their methods. This makes their outputs less realistic, as it is not reasonable to have the exact enemy positions in real-world planning. An artificial intelligence-controlled force should be able to operate without information that is not available to a human in the same situation. This dissertation presents a method for AI agents to predict and assess possible opposing force positions given typical intelligence products. We also present a method to aggregate the risk implications of these positions. We demonstrate the techniques in a combat simulation environment and evaluate their performance in multiple battle scenarios. The results show the importance of uncertainty in combat simulations and illustrate that our method of risk aggregation can be effective.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release. distribution is unlimited
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States