Supporting Autonomous Process Execution within a Multi-Perspective Constraint Frame via Numeric Planning
Artificial Intelligence
Summary
The authors describe a way to improve business process management systems by adding AI that can autonomously continue tasks while following strict rules. Unlike past work that focused mostly on simple control steps, they include more complex rules about data and timing. They created a tool that looks at what has already happened in a process and suggests the best possible next steps that follow all the given rules. Their experiments show that this method works well and can handle large tasks, helping systems make smart decisions on their own.
Authors
Paul Wittlinger, Giacomo Acitelli, Anti Alman, Fabrizio Maria Maggi, Andrea Marrella
Abstract
AI-Augmented Business Process Management Systems (ABPMS) enhance traditional BPMS by leveraging advanced AI techniques to define, execute, and monitor complex process structures. Within this landscape, Framed Autonomy denotes the capability of a system to autonomously advance the execution of a Business Process (BP) instance while strictly adhering to a predefined frame, i.e., a set of constraints that may span multiple perspectives. Existing research on framed autonomy has predominantly focused on control-flow constraints, either declarative or procedural, and typically relies on their transformation into automata-based representations. In this study, we extend this line of work by introducing a novel tool for what-if analysis that augments the process frame with multi-perspective constraints, including data-aware and temporal conditions. Given a partial process execution, the proposed approach exploits this enriched frame to recommend optimal continuations in compliance with the underlying process specifications. We additionally report an empirical evaluation demonstrating the scalability and effectiveness of the technique, thereby highlighting its potential for supporting autonomous and constraint-aware decision making in ABPMS.