@conference{keod26_pallas,
author={David Jilg and Felix Sehmer and Alexander Schultheis and Ralph Bergmann},
title={Pallas: Generic Asset Administration Shell-Based Representation of Planning Knowledge for Automatic PDDL Generation},
booktitle={Proceedings of the 18th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KEOD},
year={2026},
pages={1029-1036},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0015351600004985},
isbn={978-989-758-868-6},
issn={2184-3228},
url = {https://www.wi2.uni-trier.de/shared/publications/KEOD_2026_Pallas.pdf},
abstract = {Artificial Intelligence (AI) planning provides powerful methods for automated decision-making, but practical adoption remains limited by challenges such as the effort required to create and maintain formal planning models. In practice, planning knowledge is often encoded directly in the Planning Domain Definition Language (PDDL), which couples domain knowledge to planner-oriented syntax. Plato: The Planning Ontology addressed this with an ontology-based model for semantic planning knowledge. This paper presents Pallas: The Planning Asset Administration Shell, an Asset Administration Shell (AAS)-based counterpart for Industry 4.0 environments, where Artificial Intelligence (AI), data analytics, the Internet of Things (IoT), and distributed systems are embedded in industrial workflows. Pallas represents types, objects, actions, and related planning concepts through standard AAS submodels and supports distributed modeling across multiple AASs. The Knowledge Transformation Framework operationalizes Pallas models by merging distributed information, applying planner-specific transformations, and producing PDDL files. An initial proof-of-concept evaluation with four example domains and an exploratory user study indicates Pallas feasibility, while larger case studies remain future work.}
}

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In practice, planning knowledge is often encoded directly in the Planning Domain Definition Language (PDDL), which couples domain knowledge to planner-oriented syntax. Plato: The Planning Ontology addressed this with an ontology-based model for semantic planning knowledge. This paper presents Pallas: The Planning Asset Administration Shell, an Asset Administration Shell (AAS)-based counterpart for Industry 4.0 environments, where Artificial Intelligence (AI), data analytics, the Internet of Things (IoT), and distributed systems are embedded in industrial workflows. Pallas represents types, objects, actions, and related planning concepts through standard AAS submodels and supports distributed modeling across multiple AASs. The Knowledge Transformation Framework operationalizes Pallas models by merging distributed information, applying planner-specific transformations, and producing PDDL files. An initial proof-of-concept evaluation with four example domains and an exploratory user study indicates Pallas feasibility, while larger case studies remain future work.","bibtex":"@conference{keod26_pallas,\nauthor={David Jilg and Felix Sehmer and Alexander Schultheis and Ralph Bergmann},\ntitle={Pallas: Generic Asset Administration Shell-Based Representation of Planning Knowledge for Automatic PDDL Generation},\nbooktitle={Proceedings of the 18th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KEOD},\nyear={2026},\npages={1029-1036},\npublisher={SciTePress},\norganization={INSTICC},\ndoi={10.5220/0015351600004985},\nisbn={978-989-758-868-6},\nissn={2184-3228},\nurl = {https://www.wi2.uni-trier.de/shared/publications/KEOD_2026_Pallas.pdf},\nabstract = {Artificial Intelligence (AI) planning provides powerful methods for automated decision-making, but practical adoption remains limited by challenges such as the effort required to create and maintain formal planning models. In practice, planning knowledge is often encoded directly in the Planning Domain Definition Language (PDDL), which couples domain knowledge to planner-oriented syntax. Plato: The Planning Ontology addressed this with an ontology-based model for semantic planning knowledge. This paper presents Pallas: The Planning Asset Administration Shell, an Asset Administration Shell (AAS)-based counterpart for Industry 4.0 environments, where Artificial Intelligence (AI), data analytics, the Internet of Things (IoT), and distributed systems are embedded in industrial workflows. Pallas represents types, objects, actions, and related planning concepts through standard AAS submodels and supports distributed modeling across multiple AASs. The Knowledge Transformation Framework operationalizes Pallas models by merging distributed information, applying planner-specific transformations, and producing PDDL files. An initial proof-of-concept evaluation with four example domains and an exploratory user study indicates Pallas feasibility, while larger case studies remain future work.}\n}\n\n","author_short":["Jilg, D.","Sehmer, F.","Schultheis, A.","Bergmann, R."],"key":"keod26_pallas","id":"keod26_pallas","bibbaseid":"jilg-sehmer-schultheis-bergmann-pallasgenericassetadministrationshellbasedrepresentationofplanningknowledgeforautomaticpddlgeneration-2026","role":"author","urls":{"Paper":"https://www.wi2.uni-trier.de/shared/publications/KEOD_2026_Pallas.pdf"},"metadata":{"authorlinks":{}}},"bibtype":"conference","biburl":"www.wi2.uni-trier.de/publications/WI2Publikationen.bib","dataSources":["MSp3DzP4ToPojqkFy","9v4v84gbRB6iyKeHb","FWPbCYSxS5WYzo747","YqH7TFm9yMJqzRPy9"],"keywords":[],"search_terms":["pallas","generic","asset","administration","shell","based","representation","planning","knowledge","automatic","pddl","generation","jilg","sehmer","schultheis","bergmann"],"title":"Pallas: Generic Asset Administration Shell-Based Representation of Planning Knowledge for Automatic PDDL Generation","year":2026}