SH is an AI planning system designed to solve complex planning problems in various domains [9]. Implemented in Scala, SH is based on state-based Hierarchical Task Network (HTN) planning [6] and relies on the Hierarchical Planning Definition Language (HPDL) for specifying planning problems [7]. The SH planning system offers versatile capabilities, including modular parsing of planning problems, quick variable binding and predicate grounding on the fly during planning, resourceefficient plan generation, and seamless integration into other larger systems. These capabilities make SH suitable for addressing real-world challenges in planning, automation, assistance, and decision-making. Given its potential to contribute to various real-world applications, there is a pressing need to systematically evaluate SH’s performance and capabilities. The central research question guiding this project is: • How can the performance of the SH planning system be characterized across benchmark planning problems? To address this overarching question, the following subquestions will be explored: (a) How can benchmark HTN planning problems, specified in HDDL, be automatically translated to HPDL for processing by SH? (b) How does the SH planning system perform on benchmark HTN planning problems? (c) How does the performance of the SH planning system compare to other hierarchical planners? Keywords: AI Planning, SH Planning System, HTN Planning, Benchmark Planning, HDDL, HPDL