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Danhua Peng, Roland Ewald, and Adelinde Uhrmacher (2014)

Towards Semantic Model Composition via Experiments

In: Proceedings of the 2014 ACM SIGSIM conference on Principles of Advanced Discrete Simulation (PADS), pp. 151-162 .

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.

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