A rule-based weapon suggestion system for shipboard three dimensional defense
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- Publication date
- 1990-12
- Publisher
- Monterey, California: Naval Postgraduate School
- Collection
- navalpostgraduateschoollibrary; fedlink
- Language
- English
This thesis examines the feasibility of using an expert system approach to design an intelligent Weapon Suggestion System (WSS) to assist the Weapons Department Head (WDH) on board a naval warship in making accurate and efficient decisions in critical battle situations. We have analyzed the constraints of a WSS and the performance of on board weapons. We have also reviewed the related material previously published and discussed the implementation environment in this thesis. The system is supported by the Knowledge Engineering Environment (KEE), often referred to as an expert system shell since it provides a comprehensive set of expert system building tools to facilitate the development of expert systems. The WSS receives preprocessed sensor input, determines what contacts are present, performs target analysis and correlation based upon the current tactical situation, and suggests the most effective weapon(s) to deploy against various hostile target. Simulation results have shown that the system can provide timely decision support in a time- critical combat environment.
- Addeddate
- 2019-04-25 20:51:24
- Advisor
- Lee, Yuh-jeng
- Corporate
- Naval Postgraduate School (U.S.)
- Degree_discipline
- Engineering Science
- Degree_grantor
- Naval Postgraduate School
- Degree_level
- Masters
- Degree_name
- M.S. in Engineering Science
- Department
- Department of Computer Science
- Distributionstatement
- Approved for public release; distribution is unlimited.
- Dspace_note
- Note, the Item of Record as published can be found at https://hdl.handle.net/10945/27701.
- External-identifier
- urn:handle:10945/27701
- Foldoutcount
- 0
- Identifier
- arulebasedweapon1094527701
- Identifier-ark
- ark:/13960/t07x4069t
- Item_source
- dspace
- Ocr
- tesseract 4.1.1
- Ocr_converted
- abbyy-to-hocr 1.1.4
- Ocr_detected_lang
- en
- Ocr_detected_lang_conf
- 1.0000
- Ocr_detected_script
- Latin
- Ocr_detected_script_conf
- 0.9732
- Ocr_module_version
- 0.0.13
- Ocr_parameters
- -l eng
- Orig_md5
- f4938a3b33108cf6226d4fe76b982095
- Page_number_confidence
- 87.50
- Pages
- 90
- Ppi
- 300
- Rights
- Copyright reserved by the copyright owner
- Scanner
- Internet Archive Python library 1.8.1
- Secondreader
- Giannotti, B. B.
- Service
- Lieutenant, Republic of China Navy
- Type
- Thesis
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