arXiv:2509.00834v2 Announce Type: replace-cross Abstract: This paper addresses the problem of suffix prediction in Business Process Management (BPM) by proposing a Neuro-Symbolic Predictive Process Monitoring (PPM) approach that integrates data-driven learning with temporal logic-based prior knowledge. While recent approaches leverage deep learning models for suffix prediction, they often fail to satisfy even basic logical constraints due to the lack of explicit integration of domain knowledge during training. We propose a novel method to incorporate Linear Temporal Logic over finite traces (L
The increasing sophistication of deep learning models in business process management, combined with their inherent lack of explicit logical constraint satisfaction, drives the need for neuro-symbolic approaches.
This development is important for strategic readers as it addresses a key limitation of purely data-driven AI, enhancing reliability and interpretability in critical operational contexts for enterprises.
The explicit integration of temporal logic and domain knowledge into AI models for process prediction will lead to more robust, auditable, and constraint-satisfying automated systems.
- · Enterprise AI providers
- · Business Process Management sector
- · Finance and compliance sectors
- · Logistics and supply chain
- · Purely data-driven AI solutions
- · Companies with low AI explainability standards
Increased adoption of hybrid AI systems capable of both learning from data and adhering to predefined rules.
Improved trust and reliability of AI systems deployed in highly regulated or safety-critical business processes.
Potential for new regulatory frameworks that mandate certain levels of symbolic reasoning or explainability in AI applications.
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