# Explainable Planning for Hybrid Systems

arXiv: https://arxiv.org/abs/2604.09578 · HTML: https://mlsanity.com/paper/2604.09578/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2026.

## Abstract (arXiv)

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.