Towards High-Level Modelling in Automated Planning

TL;DR

This paper introduces an extension to the Unified-Planning Python library, enhancing its expressivity with new modeling features like arrays and integer parameters to better handle complex planning problems.

Contribution

The paper presents new high-level modeling features in Unified-Planning, including arrays, boolean counting, and integer parameters, improving its capability for complex problem representation.

Findings

Enhanced expressivity in Unified-Planning facilitates natural modeling of classical problems.

New features enable handling of more complex planning scenarios.

Demonstrated applicability through modeling three classical planning problems.

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.