Explainable Planning for Hybrid Systems
This work tackles the need for explainability in safety-critical domains like smart grids and self-driving cars, but it appears incremental as it builds on existing XAIP concepts for hybrid systems.
The paper addresses the challenge of generating explanations for AI-based planning systems in hybrid systems, which closely represent real-world problems, by presenting a comprehensive study on explainable artificial intelligence planning (XAIP).
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.