Towards semantic model composition via experiments

Abstract

Unambiguous experiment descriptions are increasingly required for model publication, as they contain information important for reproducing simulation results. In the context of model composition, this information can be used to generate experiments for the composed model. If the original experiment descriptions specify which model property they refer to, we can then execute the generated experiments and assess the validity of the composed model by evaluating their results. Thereby, we move the attention to describing properties of a model's behavior and the conditions under which these hold, i.e., its semantics. We illuminate the potential of this concept by considering the composition of Lotka-Volterra models. In a first prototype realized for JAMES II, we use ML-Rules to describe and execute the Lotka-Volterra models and SESSL for specifying the original experiments. Model properties are described in continuous stochastic logic, and we use statistical model checking for their evaluation. Based on this, experiments to check whether these properties hold for the composed model are automatically generated and executed.