Towards High-Level Modelling in Automated Planning
This work addresses the need for better modeling tools in automated planning, particularly for researchers and practitioners dealing with complex industrial or daily tasks, but it is incremental as it builds on an existing library.
The paper tackles the limited expressivity of the Planning Domain Definition Language (PDDL) in automated planning by extending the Unified-Planning library with new features like array types and integer parameters, enabling more natural high-level modeling of complex problems.
Planning is a fundamental activity, arising frequently in many contexts, from daily tasks to industrial processes. The planning task consists of selecting a sequence of actions to achieve a specified goal from specified initial conditions. The Planning Domain Definition Language (PDDL) is the leading language used in the field of automated planning to model planning problems. Previous work has highlighted the limitations of PDDL, particularly in terms of its expressivity. Our interest lies in facilitating the handling of complex problems and enhancing the overall capability of automated planning systems. Unified-Planning is a Python library offering high-level API to specify planning problems and to invoke automated planners. In this paper, we present an extension of the UP library aimed at enhancing its expressivity for high-level problem modelling. In particular, we have added an array type, an expression to count booleans, and the allowance for integer parameters in actions. We show how these facilities enable natural high-level models of three classical planning problems.