Supporting Autonomous Process Execution within a Multi-Perspective Constraint Frame via Numeric Planning

2026-07-18

AI Summary

This study addresses the limitations of existing autonomous business process execution approaches, which predominantly focus on control-flow constraints and struggle to support compliance-aware decision-making under multifaceted requirements involving data-aware and temporal conditions. To overcome this gap, the work introduces a unified multi-perspective framework that formally integrates data and time constraints through a numeric planning-based modeling approach. This enables efficient what-if analysis and optimal continuation recommendations for partially executed processes. Experimental results demonstrate that the proposed method not only ensures regulatory compliance but also exhibits strong scalability, substantially enhancing the effectiveness and practicality of autonomous decision-making in AI-augmented business process management systems.

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.

Problem

• Framed Autonomy
• Multi-Perspective Constraints
• Business Process Management
• Numeric Planning
• What-if Analysis

Innovation

• multi-perspective constraints
• numeric planning
• framed autonomy
• what-if analysis
• AI-Augmented BPMS