Towards generating and executing automatically simulation experiments

• Applicant: Professorin Dr. Adelinde Uhrmacher
• Subject Area: Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics ; Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: since 2016
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 320435134

Simulation studies are becoming more and more an indispensable tool in many application areas. However, executing simulation studies requires not only in-depth knowledge about the system to be modeled and analyzed, but also detailed knowledge about the design of simulation experiments and the involved methods. The project "towards GeneRating and Executing Automatically Simulation Experiments - GrEASE" aims at supporting systematic discrete-event stochastic simulation studies by automatically generating and executing experiments. Simulation studies involve the iterative refinement of models and the successive execution of diverse experiments, e.g., sensitivity analysis or optimization, for which again different methods are available. To automatically generate and execute simulation experiments, we will focus on the following questions: what kinds of knowledge about simulation experiments, methods, goals, and about the current simulation study are needed, how can such knowledge be represented, used, and combined, and what role can schemas of simulation experiments, ontologies about methods, the conceptual model, as well as previously executed simulation experiments and provenance play in this endeavor? We will pursue two strategies to generate simulation experiments. Both rely on an effective combination of the above knowledge sources. However, their starting points and approaches vary. One strategy focuses on generating and executing a specific simulation experiment on the basis that experiments with similar goals have been executed before, or on the basis that certain experiments have been done with closely related models. Thus, provenance will form the starting point for this strategy. The second strategy aims to generate simulation experiments from scratch and will rely on inference rules to select an experiment type, appropriate methods, and to fill the respective experiment templates. Here, the conceptual model will be crucial as it provides context information about the simulation model. Several specific simulation studies will help evaluating the developed methods, strategies, and their combination.

• DFG Programme: Research Grants