{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:07:54Z","timestamp":1781366874534,"version":"3.54.1"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Changes in the information age have induced the necessity for a more efficient and effective self-decision-making requirement. A method of extracting and constructing naval operations decision-making rules based on scenario analysis is proposed. The template specifications of Event Condition Action (ECA) rules are defined, and a consistency detection method of ECA rules based on SWRL is proposed. The logical relationships and state transitions of the naval operational process is analyzed in detail, and the association of objects, events, and behaviors is realized. Finally, the operation of the proposed methods is illustrated through an example process, showing the method can effectively solve the problems of self-decision-making rule extraction and construction among naval battlefield decision environment, and avoid relying on artificial intelligence, which may have brought some uncertain factors.<\/jats:p>","DOI":"10.3390\/info13030144","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T01:53:54Z","timestamp":1646790834000},"page":"144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Operational Rule Extraction and Construction Based on Task Scenario Analysis"],"prefix":"10.3390","volume":"13","author":[{"given":"Xinye","family":"Zhao","sequence":"first","affiliation":[{"name":"Operational Software and Simulation Institute, Dalian Naval Academy, Dalian 116018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Operational Software and Simulation Institute, Dalian Naval Academy, Dalian 116018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Cui","sequence":"additional","affiliation":[{"name":"Operational Software and Simulation Institute, Dalian Naval Academy, Dalian 116018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangming","family":"Sun","sequence":"additional","affiliation":[{"name":"Operational Software and Simulation Institute, Dalian Naval Academy, Dalian 116018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209776","DOI":"10.1109\/ACCESS.2020.3034225","article-title":"Hierarchical Reinforcement Learning for Autonomous Decision Making and Motion Planning of Intelligent Vehicles","volume":"8","author":"Lu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","first-page":"1","article-title":"Supply Chain Risk Prevention and Control Based on Fuzzy Influence Diagram and Discrete Hopfield Neural Network","volume":"2021","author":"Su","year":"2021","journal-title":"Discret. 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