Reduction of complexity for the analysis of human-machine-interaction

In this contribution, a concept and principal realization of an additional module within a proposed HMI analysis architecture is developed. The main aspect of this module is the reduction of complexity allowing the analysis of a Human-Machine-System. Core of the architecture is an action model, which is methodical founded on Situation-Operator-Modeling. The action model describes the interaction within a Human-Machine-System and is implemented by high-level Petri Nets. From the Petri-Net-model a state space can be generated to analyze the interaction between a human operator and the environment (in general assumed as machine). The definition of the situation representing the considered part of the real world influences the size of the state space significantly. This contribution realizes the implementation of a size-variable situation vector to reduce the complexity of the considered system. The functionality of the extended architecture is illustrated by the interaction of a human operator with an arcade game.