Reinforcement learning applications to combat identification
| dc.contributor.advisor | Zhao, Ying | |
| dc.contributor.advisor | Kendall, Walter | |
| dc.contributor.author | Mooren, Emily M. | |
| dc.contributor.department | Information Sciences (IS) | |
| dc.date | Mar-17 | |
| dc.date.accessioned | 2017-05-10T16:31:54Z | |
| dc.date.available | 2017-05-10T16:31:54Z | |
| dc.date.issued | 2017-03 | |
| dc.description.abstract | Crucial to the safe and effective operation of U.S. Navy vessels is the quick and accurate identification of aircraft in the vicinity. Modern technology and computer-aided decision-making tools provide an alternative to dated methods of combat identification. By utilizing the Soar Cognitive Architecture's reinforcement learning capabilities in conjunction with combat identification techniques, this thesis explores the potential for the collaboration of two. After developing a basic interface between Soar and combat identification methods, this thesis analyzes the overall correctness of the developed Soar agent to established truths in an effort to ascertain the level of system learning. While the scope of this initial research is limited, the results are favorable to a dramatic modernization of combat identification. In addition to establishing proof of concept, these findings can aid future research to develop a robust system that can mimic and/or aid the decision-making abilities of a human operator. While this research does focus on a sea-based, naval, application, the findings can also be expanded to DOD-wide implementations. | en_US |
| dc.description.distributionstatement | Approved for public release; distribution is unlimited. | |
| dc.description.recognition | Outstanding Thesis | |
| dc.description.service | Lieutenant Commander, United States Navy | en_US |
| dc.description.uri | http://archive.org/details/reinforcementlea1094553020 | |
| dc.identifier.uri | https://hdl.handle.net/10945/53020 | |
| dc.publisher | Monterey, California: Naval Postgraduate School | en_US |
| dc.relation.ispartofseries | NPS Outstanding Theses and Dissertations | |
| dc.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. | en_US |
| dc.subject.author | reinforcement learning | en_US |
| dc.subject.author | combat identification | en_US |
| dc.title | Reinforcement learning applications to combat identification | en_US |
| dc.type | Thesis | en_US |
| dspace.entity.type | Publication | |
| etd.thesisdegree.discipline | Network Operations and Technology | en_US |
| etd.thesisdegree.grantor | Naval Postgraduate School | en_US |
| etd.thesisdegree.level | Masters | en_US |
| etd.thesisdegree.name | Master of Science in Network Operations and Technology | en_US |
| relation.isSeriesOfPublication | c5e66392-520c-4aaf-9b4f-370ce82b601f | |
| relation.isSeriesOfPublication.latestForDiscovery | c5e66392-520c-4aaf-9b4f-370ce82b601f |
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