Machine learning (ML) algorithms can prove to be instrumental in certain complex ill-con-ditioned systems when inserted as a middle layer to interface low-level hardware, such as sensors and actuators, and high-level decision-making kernels. Such an interface provides a secondary, or supervisory, conditioning layer that would enhance the system's robustness in the face of various types of uncertainties and disturbances. This article elaborates how ML can leverage the solution of a contemporary problem related to the security of maritime domains.

中文翻译：

---

使用强化学习应对加拿大海岸线的非法活动


当机器学习 (ML) 算法作为中间层插入以连接低级硬件（例如传感器和执行器）以及高级决策内核时，可以证明在某些复杂的不良系统中非常有用。这样的接口提供了辅助或监督调节层，可以增强系统面对各种类型的不确定性和干扰时的鲁棒性。本文详细阐述了机器学习如何利用解决与海域安全相关的当代问题。