Planning with Complex Data Types in PDDL

TL;DR

This paper extends PDDL to support complex data types like sets, arrays, and records, enabling more practical modeling of real-world problems involving complex data structures.

Contribution

It introduces a translation approach that maps complex data types to Boolean logic and then to PDDL, broadening the applicability of PDDL planners.

Findings

The translation effectively models complex data types in PDDL.

Practical solutions are provided for issues in PDDL translation.

The approach enables planning in complex software systems.

Abstract

Practically all of the planning research is limited to states represented in terms of Boolean and numeric state variables. Many practical problems, for example, planning inside complex software systems, require far more complex data types, and even real-world planning in many cases requires concepts such as sets of objects, which are not convenient to express in modeling languages with scalar types only. In this work, we investigate a modeling language for complex software systems, which supports complex data types such as sets, arrays, records, and unions. We give a reduction of a broad range of complex data types and their operations to Boolean logic, and then map this representation further to PDDL to be used with domain-independent PDDL planners. We evaluate the practicality of this approach, and provide solutions to some of the issues that arise in the PDDL translation.