Formalization and automated detection of human errors

The contribution describes a novel approach for the detection and classification of human errors in interaction with complex dynamic systems, according to Donierpsilas error taxonomy. The programmed implementation of the approach based on situation-operator-modeling (SOM) is already realized using software tools for high-level Petri nets (HPN). An experimental environment consisting of an arcade style game communicating with the HPN-software CPN Tools is used. With CPN Tools, the interaction between a human operator and the arcade game is modeled and further mapped to an automatically generated state space. Using generically formulated state space queries, the human error dasiarigiditypsila is detected.