Against the Clock: Lessons Learned by Applying Temporal Planning in Practice

Automated Planning is a foundational area of AI research, focusing on the automated synthesis of courses of actions to achieve a desired goal within a formally-modeled system. When dealing with time and temporal constraints, this problem is known as Temporal Planning. In this paper, we will present our research on the application of temporal planning to real-world scenarios, and highlight the open research directions in this field. Starting from a series of projects in different application domains – including robotics, manufacturing, and logistics – we will explore key challenges encountered, the (sometimes hard) lessons learned, and the techniques, tools, and methodologies that have emerged from these efforts. Additionally, we will introduce and discuss preliminary results on applying Reinforcement Learning techniques to tailor temporal planners to specific application contexts.