Towards High-Level Modelling in Automated Planning
Carla Davesa Sureda, Joan Espasa Arxer, Ian Miguel, Mateu Villaret Auselle
TL;DR
The paper tackles the expressivity limitations of PDDL by proposing high-level modelling within Unified-Planning (UP). It introduces ArrayA, CountSupport, and integer-parameter actions, along with compilers that translate high-level constructs into lower-level representations suitable for planners. The authors describe the UP pipeline, including problem-instance construction, lifted representations, and automatic transformations to ensure compatibility across diverse planners, with support for exporting to formats like PDDL or ANML. Through experiments on grid-based problems such as Plotting, Rush Hour, and 8-Puzzle, the work demonstrates that high-level modelling can yield more natural problem descriptions while remaining solver-friendly, advancing practical planning workflows.
Abstract
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.