Learning and control in trustworthy and responsible artificial intelligence for cyber-physical systems
Creators: name: Xu, Lingyi
Publisher: name: No Publisher Supplied
Year: 2024

description: With breakthroughs in technology and excellence in robustness, artificial intelligence (AI) has gradually reshaped the way of information interaction and has been challenging traditional cyber-physical systems (CPS) research. Nourished by enormous data, AI and data-driven machine learning have been emerging with CPS ideas for better capability and adaptability in automation and autonomy. Yet, concerns about the emerging ideas have been raised on dependability and accountability. The data-driven models have been proven to over-reliance on training data, which exposes unreliable generalization and is sensitive to data disturbances. It is true that a larger and broader training dataset could tend to a better model, however, gathering such a dataset is a time-consuming, labor-intensive, and cost-demanding progress with threats to individual privacy. Therefore, it is inevitable to find ethical and robust alternative solutions, among which an interest is in trustworthy and responsible AI. This dissertation explores the learning and control in trustworthy and responsible AI for CPS from a control perspective. In particular, this dissertation intends to improve the performance of data-driven learning on non-cooperative facial-credentialed security authentication within complex backgrounds. Limited by the physical and cyber components, the quality of visual data within CPS is generally worse than that in datasets. Typically, the collection of datasets for facial-credentialed security authentication tends to address variation in pose, size, illumination, and occlusion but not in facial quality (scales). However, facial quality is an inevitable factor affecting non-cooperative facial-credentialed security authentication. Feature Super-Resolution Face Recognition Net (FSRFR-Net) is designed to balance the quality-effect on facial-credentialed recognition. It utilizes the trained data-driven models, which are convolutional neural networks trained on high-quality faces, and improves their performance on low-quality faces with a minimum change, which is to insert a multi-branch feature super resolution module, in the structure. Experiments on different datasets present consistency in performance improvement on low-quality recognition while preserving high-quality recognition performance. The performance improvement difference among datasets demonstrates the high correlation between the larger face region and better recognition performance.Following that observation, an anchor-free duo-agent reinforcement learning model is proposed for multi-scale (multi-quality) face detection. It measures the image quality caused influence from the weighted combination of channel-wise feature maps. The design of duo-agent allows the freedom in the pixel-size (resolution) of faces while preserving computational simplicity. The detection goal is to maximize the facial region while minimize the background region. In addition, the duo-agent reinforcement learning design transforms the face detection problem into a classic global vs. local extrema regression problem in the 3D feature space. Consequentially, an intention is to find a stable and convergence solution to the regression problem. In order to solve the stochastic discrete-time model-free duo-agent regression problem, the convergence efficiency and stability of reinforcement learning approaches in CPS have been explored from the control theory perspective. A mild condition has been proven applicability to policy iteration approach of zero-sum differential games and H∞ robust optimal control. A Simultaneous Online Tracking and Planning (SOTP) Navigation system of autonomous tracked vehicles for leader-follower formation is proposed to examine the optimal strategies obtained through policy iteration approach of multi-agent linear-quadratic optimal control problems. The SOTP framework is analyzed and designed through a skid-steering dynamic model with optimization of low-level controls (agents) of autonomous tracked vehicles for the leader-follower formation. Simulation results have shown the efficacy and robustness of the SOTP framework while the freedom of trajectory curvature and formation posture are reserved without the virtual followers/leaders.