From Capability Models to Automated Planning: An AAS-Native Approach for Automatic PDDL Generation
2026-06-01 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors show a way to automatically create planning problems from digital models called Asset Administration Shells (AAS), used in Industry 4.0 production systems. Normally, setting up these planning problems requires special knowledge of a language called PDDL, which engineers often lack. Their method translates detailed descriptions of resource capabilities in AAS directly into PDDL problems without needing engineers to deal with complex planning languages. They tested this approach on a lab production system to compare different layouts and help engineers decide which designs work best by simply updating the AAS model.
Asset Administration Shell (AAS)Industry 4.0Production SystemsPlanning Domain Definition Language (PDDL)Automated PlanningDigital TwinVDI 3682IEC 61360-1IDTA 02011IDTA 02016
Authors
Hamied Nabizada, Thomas Wirt, Luis Miguel Vieira da Silva, Felix Gehlhoff, Alexander Fay
Abstract
Engineers designing production systems need to verify that a given layout supports all required production sequences. Automated planning techniques can answer such questions, but formulating the required planning problems in the Planning Domain Definition Language (PDDL) demands specialized expertise that production engineers typically lack. Asset Administration Shells (AAS) have emerged as the standardized Digital Twin for industrial assets in Industry 4.0. We show that AAS capability models, structured using four established Industry 4.0 standards (VDI 3682 for process descriptions, IEC 61360-1 for semantic property qualification, IDTA 02011 for type hierarchies, and IDTA 02016 for instance descriptions), contain sufficient information to generate complete PDDL problems automatically. Unlike prior work that introduced PDDL-specific submodels, our approach derives all planning elements from domain-level descriptions of resource functions, so-called capabilities, allowing engineers to model capabilities without any exposure to PDDL syntax or planning concepts. Our extraction algorithm transforms distributed Multi-AAS architectures into complete PDDL planning problems. We validate the approach on AAS models of a laboratory production system, comparing four layout variants using optimal planning to demonstrate how engineers can systematically explore design trade-offs by modifying the AAS model and regenerating the planning domain