Highlights
- Core Innovation: Combining neural networks with symbolic reasoning to create AI systems that learn from data while respecting business rules and compliance constraints
- Key Approach: Multi-level knowledge injection into ML models—at input, hidden, and output layers—ensuring predictions are both accurate and compliant
- Main Impact: Enabling trustworthy AI deployment in compliance-critical domains (healthcare, finance) where violations of business rules have severe consequences
Background
Business process management has evolved from manual workflow documentation to sophisticated data-driven systems that leverage machine learning for process optimization, anomaly detection, and predictive monitoring. Organizations across healthcare, finance, manufacturing, and logistics generate massive amounts of event logs that capture detailed execution traces of their operational processes. While deep learning approaches have shown remarkable success in learning patterns from these event logs, they often operate as black boxes that fail to incorporate critical domain knowledge, business rules, and regulatory constraints. This limitation becomes particularly problematic in compliance-critical domains where violations of business rules can have severe legal, financial, or safety implications. The emerging field of neuro-symbolic AI offers a promising paradigm that combines the pattern recognition capabilities of neural networks with the explicit reasoning and constraint satisfaction of symbolic systems, potentially bridging the gap between data-driven learning and knowledge-driven compliance.
Problem Definition
This project aims to develop a comprehensive framework for injecting process knowledge into machine learning models for intelligent process management. The framework will address the fundamental challenge of ensuring that ML-based process predictions and recommendations respect domain constraints while maintaining high predictive accuracy. We will create methodologies that: (1) systematically extract and formalize different types of process knowledge from domain experts and process documentation, (2) develop novel architectures that integrate this knowledge at multiple levels of neural network processing, (3) handle conflicts between learned patterns and explicit constraints, and (4) maintain model interpretability while preserving the flexibility of deep learning approaches.
Task
The project aims to conduct the following research:
- Investigate state-of-the-art neuro-symbolic AI techniques and their applicability to process analytics
- Develop a taxonomy of process knowledge types (control-flow constraints, temporal dependencies, resource constraints, compliance rules) and their formal representations using Linear Temporal Logic (LTL) and first-order logic
- Design multi-level knowledge injection mechanisms that integrate constraints at the input layer (feature engineering), hidden layers (constraint-guided attention), and output layer (constrained decoding)
- Create conflict resolution strategies for handling inconsistencies between historical data patterns and domain rules
- Build evaluation frameworks that assess both predictive accuracy and constraint satisfaction
- Develop interactive tools for domain experts to specify and validate process constraints without requiring formal logic expertise
- Implement case studies in healthcare process management and financial compliance monitoring
Topics
- Neuro-Symbolic Artificial Intelligence
- Process Mining and Predictive Process Monitoring
- Temporal Logic and Constraint Programming
- Knowledge Representation and Reasoning
- Explainable AI for Process Management
- Logic Tensor Networks and Differentiable Reasoning
- Attention Mechanisms with Symbolic Constraints
- Hybrid Learning Architectures
Expected Outcomes
- A comprehensive framework for categorizing and formalizing process knowledge suitable for neural integration
- Novel neural architectures that incorporate process constraints through attention mechanisms, custom loss functions, and constraint layers
- Algorithms for automated extraction of implicit constraints from process event logs
- Conflict resolution strategies that balance learned patterns with mandatory constraints
- An open-source toolkit implementing the proposed neuro-symbolic process monitoring framework
- Benchmark datasets annotated with process constraints for evaluating constraint-aware predictive models
- Empirical validation through case studies in healthcare pathway prediction and financial transaction monitoring
- Guidelines for practitioners on when and how to apply different knowledge injection strategies
Potential Impact
This project will significantly advance the field of intelligent process management by making machine learning models more trustworthy and deployment-ready in compliance-critical domains. Organizations will be able to leverage the power of deep learning while ensuring adherence to regulatory requirements and business rules. The framework will be particularly valuable in sectors like healthcare, where process predictions must respect clinical guidelines, and finance, where regulatory compliance is mandatory. By providing interpretable models that explicitly incorporate domain knowledge, the framework will increase stakeholder trust in AI-driven process recommendations. Furthermore, the methodology will reduce the amount of training data required to achieve reliable performance, as domain constraints can compensate for sparse examples of certain process behaviors. The open-source toolkit will democratize access to constraint-aware process mining techniques, enabling smaller organizations and research groups to develop sophisticated process intelligence systems that respect domain-specific requirements.
Contact
g.park@tue.nl