[2603.26461] Neuro-Symbolic Process Anomaly Detection

Computer Science > Machine Learning arXiv:2603.26461 (cs) [Submitted on 27 Mar 2026] Title:Neuro-Symbolic Process Anomaly Detection Authors:Devashish Gaikwad, Wil M. P. van der Aalst, Gyunam Park View a PDF of the paper titled Neuro-Symbolic Process Anomaly Detection, by Devashish Gaikwad and 2 other authors View PDF HTML (experimental) Abstract:Process anomaly detection is an important application of process mining for identifying deviations from the normal behavior of a process. Neural network-based methods have recently been applied to this task, learning directly from event logs without requiring a predefined process model. However, since anomaly detection is a purely statistical task, these models fail to incorporate human domain knowledge. As a result, rare but conformant traces are often misclassified as anomalies due to their low frequency, which limits the effectiveness of the detection process. Recent developments in the field of neuro-symbolic AI have introduced Logic Tensor Networks (LTN) as a means to integrate symbolic knowledge into neural networks using real-valued logic. In this work, we propose a neuro-symbolic approach that integrates domain knowledge into neural anomaly detection using LTN and Declare constraints. Using autoencoder models as a foundation, we encode Declare constraints as soft logical guiderails within the learning process to distinguish between anomalous and rare but conformant behavior. Evaluations on synthetic and real-world datasets ...