让复杂系统的智能规划过程可解释，提升安全关键场景的可信度。
Explainable Planning for Hybrid Systems
- 基于混合系统建模，实现对复杂动态行为的可解释规划。
- 提出可解释性框架，支持对规划决策路径的逐层追溯与可视化。
- 适用于自动驾驶、医疗系统等高风险领域，适合关注AI可信性的研究者。
人工智能技术的进步推动了自动化范式转型，自主系统正逐步替代人工设计的系统。自动化规划是这些系统的核心。随着强大规划器的发展，自动化规划已应用于智能电网、自动驾驶、仓储自动化、城市与空中交通管制、搜救行动、监控、机器人及医疗等多个复杂且关乎安全的领域。当前，生成对基于AI系统的解释成为该领域面临的主要挑战之一。本文针对能够贴近真实世界问题表示的混合系统，开展了可解释人工智能规划（XAIP）的全面研究。
原文摘要 · Abstract (English)
The recent advancement in artificial intelligence (AI) technologies facilitates a paradigm shift toward automation. Autonomous systems are fully or partially replacing manually crafted ones. At the core of these systems is automated planning. With the advent of powerful planners, automated planning is now applied to many complex and safety-critical domains, including smart energy grids, self-driving cars, warehouse automation, urban and air traffic control, search and rescue operations, surveillance, robotics, and healthcare. There is a growing need to generate explanations of AI-based systems, which is one of the major challenges the planning community faces today. The thesis presents a comprehensive study on explainable artificial intelligence planning (XAIP) for hybrid systems that capture a representation of real-world problems closely.
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