On the Hybrid Nature of ABPMS Process Frames and its Implications on Automated Process Discovery

2026-04-24

AI Summary

Traditional business process models struggle to balance normative control with openness, limiting the flexible autonomy of AI-enhanced Business Process Management Systems (ABPMS). This work proposes a hybrid process framework that integrates procedural and declarative models through semi-concurrent execution. It reinterprets procedural models under the open-world assumption as constraint mechanisms for the first time, establishing an equivalence mapping between such models and declarative constraints like Declare. Building on this insight, the paper develops a formal semantic foundation for hybrid processes and introduces an automated method to map declarative constraints into semi-concurrent procedural fragments. This theoretical advancement lays the groundwork for more intelligent and adaptable automated process discovery.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Intelligent Query Processing
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

A core component of any AI-Augmented Business Process Management System (ABPMS) is the process frame, which gives the system process-awareness and defines the boundaries in which the system must operate. Compared to traditional process models, the process frame should, in principle, provide a somewhat more permissive representation of the managed processes, such that the (semi) autonomous behavior of an ABPMS, referred to as framed autonomy, could emerge. At the same time, it is not limited to a single linguistic or symbolic formalism and may incorporate heterogeneous knowledge ranging from predefined procedures to commonsense rules and best practices. In this paper, we conceptualize the notion of an ABPMS process frame as a hybrid business process representation, consisting of semi-concurrently executed procedural and declarative process models. We rely on our earlier works to outline the execution semantics of this type of process frame, arguing in favor of adopting the open-world assumption of the declarative paradigm also for procedural process models. The latter leads to a constraint-like interpretation, where each procedural model is considered to constrain the activities within that model, without imposing explicit execution requirements nor limitations on activities that may be present in other models. This is analogous to existing declarative languages, such as Declare, where each constraint has a direct effect only on the specific activities being constrained. Given this similarity, we propose mapping subsets of discovered declarative constraints into equivalent semi-concurrently executed procedural fragments, thus laying the foundation for a corresponding process (frame) discovery approach.

Problem

• ABPMS
• process frame
• hybrid process representation
• framed autonomy
• automated process discovery

Innovation

• hybrid process representation
• framed autonomy
• open-world assumption
• declarative-procedural integration
• process frame discovery