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

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"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.