Explainable Planning for Hybrid Systems

Computer Science > Artificial Intelligence [Submitted on 24 Feb 2026] Explainable Planning for Hybrid Systems Mir Md Sajid Sarwar 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. Comments: PhD thesis Subjects: Artificial Intelligence (cs.AI) ACM classes: I.2; F.0 Cite as: arXiv:2604.09578 [cs.AI]   (or arXiv:2604.09578v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.09578 Focus to learn more Submission history From: Mir Md Sajid Sarwar [view email] [v1] Tue, 24 Feb 2026 21:24:29 UTC (38,250 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)