BPMN to PDDL: Translating Business Workflows for AI Planning

2025-11-22

AI Summary

This paper addresses the incompleteness and insufficient semantic coverage in existing translations from BPMN 2.0 to the AI planning language PDDL. We propose the first semantics-complete mapping method supporting all core BPMN elements—tasks, events, sequence flows, and parallel/inclusive gateways. Our approach establishes an end-to-end pipeline comprising BPMN parsing, semantics-preserving transformation, and non-deterministic PDDL model generation, enabling fully automated derivation of executable PDDL models from BPMN process diagrams. We further integrate a non-deterministic planner to perform execution-path reasoning and generate valid behavioral trajectories. Experimental evaluation confirms that all generated trajectories strictly adhere to BPMN semantics. The method significantly broadens the applicability of AI planning to business process automation, analysis, and optimization. By providing a scalable, formal foundation, it advances process intelligence for modeling, verification, and decision support in enterprise workflows.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Translating BPMN workflows into AI planning representations
• Supporting core BPMN constructs for automated planning
• Bridging theoretical models with practical planning implementations

Innovation

• Translating BPMN 2.0 diagrams into PDDL representations
• Supporting core BPMN constructs and gateway behaviors
• Using non-deterministic planner to generate execution traces