CNS Core: Small: PilotPC: Proactive Inverse Learning of Network Topology for Predictive Communication among Unmanned Vehicles

如今，无人驾驶飞行器（uav）的使用已经在许多应用中变得司空见惯。与许多其他技术一样，无人机在许多应用中为当前的实践带来了舒适性和效率，同时也带来了威胁和安全问题。该项目将推进科学前沿，开发更安全、更可靠的空中监测平台，同时提供隐私和安全。为了实现这一目标，本项目旨在通过使用逆学习技术在无人机网络中进行准确的预测，从而实现更安全、更高效的操作。将开发智能观察系统，持续监测自主主体的行为，通过测量其在给定条件下偏离最理性行为来识别可疑活动。建议的研究包括以下几个步骤：(i)开发机制来模拟目标节点对周围环境的感知（即，使用深度学习方法从目标的眼睛看到单词）；（ii）通过反向学习（读取目标的大脑）对目标的决策过程进行建模，在给定条件和可用资源下预测其理性行为；（iii）通过检测偏离其预期理性行为的罕见但关键的事件来衡量信任水平。特别是，无人机的移动性及其网络活动将作为恶意活动的两个重要场景进行研究。该项目预计将开发用于智能节点动作分析的新工具和算法，并将与研究界共享。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The use of unmanned aerial vehicles (UAVs) has become commonplace in many applications nowadays. Like many other technologies, UAVs brings comfort and efficiency to the current practice in many applications while posing threats and safety issues. This project will advance the frontier of science in developing safer and more reliable aerial monitoring platforms while offering privacy and safety. Towards this goal, this project aims to make accurate predictions in a network of unmanned aerial vehicles, by using inverse learning techniques, so as to enable safer and more efficient operations.A smart observation system will be developed to constantly monitor the behavior of autonomous agents to identify suspicious activities by measuring their deviation from the most rational behavior under given conditions. The proposed research includes several steps: (i) to develop mechanisms to simulate the target node's perception of its surrounding environment (i.e., seeing the word from the target's eyes) using deep learning methods, (ii) to predict its rational behavior(s) under given conditions and available resources by modeling the target's decision making procedure through inverse learning (reading the target's brain), and (iii) to measure the level of trust by detecting rare but critical episodes of deviating from its expected rational behavior. In particular, the mobility of UAVs and their networking activities will be investigated as two important scenarios of malicious activities. This project is expected to result in developing new tools and algorithms for the action analysis of smart nodes that will be shared with the research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.