PREDICTING OPPONENT POSITION AND MODELING UNCERTAINTY
Maroon, Kenneth J.
Buss, Arnold H.
Appleget, Jeffrey A.
Darken, Christian J.
Alt, Jonathan K.
Balogh, Imre L.
MetadataShow full item record
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.
RightsThis 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
Showing items related by title, author, creator and subject.
Nagashima, M.; Agrawal, B.N. (2012);For a large Adaptive Optics (AO) system such as a large Segmented Mirror Telescope (SMT), it is often difficult, although not impossible, to directly apply common Multi-Input Multi-Output (MIMO) controller design methods ...
A Method to Choose Between Automation and Human Operators for Recovery Actions During a Cyber Attack for Recovery Actions During a CyberAttack Van Bossuyt, Douglas L. (Elsevier, 2019);As complex systems such as nuclear power plants, naval ships, critical infrastructure, and other systems become more connected system increases. In many systems, recovery actions can prevent an incipient failure from causing ...
Analyzing the Effects of Source Selection Method, Acquisition Type, and Service Component on Acquisition Outcomes Landale, Karen A. F.; Rendon, Rene G. (Monterey, California. Naval Postgraduate School, 2017-03); SYM-AM-17-043For years, one of the most hotly contested debates in contracting and acquisition has been the choice of source selection method and the contract-related consequences of that choice. While policy memos encourage contracting ...