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| Auteurs principaux: | , , |
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| Format: | Preprint |
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2025
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| Accès en ligne: | https://arxiv.org/abs/2511.18171 |
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| _version_ | 1866915634486444032 |
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| author | Nie, Jasper Muise, Christian Armstrong, Victoria |
| author_facet | Nie, Jasper Muise, Christian Armstrong, Victoria |
| contents | Business Process Model and Notation (BPMN) is a widely used standard for modelling business processes. While automated planning has been proposed as a method for simulating and reasoning about BPMN workflows, most implementations remain incomplete or limited in scope. This project builds upon prior theoretical work to develop a functional pipeline that translates BPMN 2.0 diagrams into PDDL representations suitable for planning. The system supports core BPMN constructs, including tasks, events, sequence flows, and gateways, with initial support for parallel and inclusive gateway behaviour. Using a non-deterministic planner, we demonstrate how to generate and evaluate valid execution traces. Our implementation aims to bridge the gap between theory and practical tooling, providing a foundation for further exploration of translating business processes into well-defined plans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18171 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BPMN to PDDL: Translating Business Workflows for AI Planning Nie, Jasper Muise, Christian Armstrong, Victoria Artificial Intelligence I.2.8; D.2.11 Business Process Model and Notation (BPMN) is a widely used standard for modelling business processes. While automated planning has been proposed as a method for simulating and reasoning about BPMN workflows, most implementations remain incomplete or limited in scope. This project builds upon prior theoretical work to develop a functional pipeline that translates BPMN 2.0 diagrams into PDDL representations suitable for planning. The system supports core BPMN constructs, including tasks, events, sequence flows, and gateways, with initial support for parallel and inclusive gateway behaviour. Using a non-deterministic planner, we demonstrate how to generate and evaluate valid execution traces. Our implementation aims to bridge the gap between theory and practical tooling, providing a foundation for further exploration of translating business processes into well-defined plans. |
| title | BPMN to PDDL: Translating Business Workflows for AI Planning |
| topic | Artificial Intelligence I.2.8; D.2.11 |
| url | https://arxiv.org/abs/2511.18171 |