Online Dynamic Graphical Game Approach for Pursuing Illegal Fishing Vessels with Running Energy Optimization**

通过运行能量优化追捕非法渔船的在线动态图形游戏方法**

• 批准号: 537568-2018
• 负责人: Gueaieb, Wail
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=652591
• 关键词: Online; Dynamic; Graphical; Game; Approach

"Illegal, unreported, and unregulated" (IUU) fishing events are having severe environmental and economic consequences on Canadian water resources. They lead to harmful changes in the ecosystem, depleting the natural resources, and causing major financial losses. This urges for the development of innovative solution platforms to autonomously detect, track, and pursue IUU fishing events during the monitoring of maritime territories. This leads to an interesting structured engineering problem with several technical challenges. First, enhancing the ability of each pursuing vessel to track and detect IUU fishing events in an efficient manner. Second, enhancing the capability of the pursuing vessels to communicate among each other through an effective communication or information layer that helps achieving their cooperative objectives (e.g., getting closer to the illegal vessel, while exchanging the necessary information with the neighboring pursuing vessels) and global objectives (e.g., collectively achieving the final objective by the participating vessels). Third, optimizing the performance of each pursuing vessel, by minimizing its dissipated energy during the monitoring process, for instance. These objectives can be tackled using recent advances in artificial intelligence (AI). To this end, the proposed research aims at devising a solution that brings together ideas from machine learning (ML), game theory, computational graph theory, optimal control, and neurofuzzy systems. The proposed approach is based on designing online game-theoretic approaches which take advantage of state-of-the-art reinforcement learning schemes to learn the behavior of illegal vessels. Game theory is applied to understand and model the interactive nature of the multi-agent system (the pursuing against the illegal vessels). Optimal control theory formulates the optimization problem along with the different applicable constraints leading to the optimality conditions at which the different sub-systems act in an optimized fashion. This research will help advancing the state of knowledge of ML, which is in line with the nation's strategic plan to build reputed AI hubs.

“非法，未报告和不受监管的”（IUU）捕鱼事件对加拿大水资源造成了严重的环境和经济后果。它们导致生态系统有害变化，耗尽自然资源并造成重大财务损失。这敦促开发创新的解决方案平台，以自主检测，追踪和追踪IUU捕鱼活动，并在监测海上领土。这导致了一个有趣的结构化工程问题，并带来了一些技术挑战。首先，增强了每次追求船只以有效地跟踪和检测IUU捕鱼事件的能力。其次，通过有效的沟通或信息层来增强追捕船只之间相互交流的能力，这些沟通或信息层有助于实现其合作目标（例如，与非法船只更接近非法船只，同时与相邻的追捕船只交换必要的信息）和全球目标（例如，通过参与船只共同实现最终目标）。第三，通过在监测过程中最大程度地减少其消散的能量来优化每个追捕船的性能。可以使用人工智能（AI）的最新进展来解决这些目标。为此，拟议的研究旨在设计一种解决方案，该解决方案将机器学习（ML），游戏理论，计算图理论，最佳控制和神经模糊系统的想法汇集在一起​​。所提出的方法基于设计在线游戏理论方法，该方法利用最先进的强化学习方案来学习非法船只的行为。游戏理论用于理解和建模多代理系统的互动性质（对非法船只的追求）。最佳控制理论将优化问题以及不同的适用约束，从而导致不同子系统以优化方式起作用的最佳条件。这项研究将有助于促进ML知识状态，这符合该国建立著名的AI枢纽的战略计划。