| DOI | Resolve DOI: https://doi.org/10.1609/aaai.v38i21.30439 |
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| Author | Search for: Fan, Yimeng; Search for: Agand, Pedram; Search for: Chen, Mo; Search for: Park, Edward J.; Search for: Kennedy, Allison1; Search for: Bae, Chanwoo |
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| Affiliation | - National Research Council Canada. Ocean, Coastal and River Engineering
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| Format | Text, Article |
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| Conference | 38th AAAI Conference on Artificial Intelligence, February 23, 2024, Vancouver, British Columbia, Canada |
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| Subject | sequential modeling; transformer architecture; offline RL dataset; marine navigation; gym environment; auto-regressive model |
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| Abstract | The maritime industry's continuous commitment to sustainability has led to a dedicated exploration of methods to reduce vessel fuel consumption. This paper undertakes this challenge through a machine learning approach, leveraging a real-world dataset spanning two years of a passenger vessel in west coast Canada. Our focus centers on the creation of a time series forecasting model given the dynamic and static states, actions, and disturbances. This model is designed to predict dynamic states based on the actions provided, subsequently serving as an evaluative tool to assess the proficiency of the vessel's operation under the captain's guidance. Additionally, it lays the foundation for future optimization algorithms, providing valuable feedback on decision-making processes. To facilitate future studies, our code is available at https://github.com/pagand/model_optimze_vessel/tree/AAAI. |
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| Publication date | 2024-03-25 |
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| Publisher | Association for the Advancement of Artificial Intelligence |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 670746eb-92d4-445b-a6ee-4f3898c0533f |
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| Record created | 2025-11-18 |
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| Record modified | 2026-02-18 |
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