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Authors: Osama Maqbool and Jürgen Roßmann

Affiliation: Institute for Man-Machine-Interaction, RWTH Aachen University, Germany

Keyword(s): Logical Scenarios, Randomization, Synthetic Data.

Abstract: Simulations and synthetic data are a necessary supplement to real-world experiments in order to alleviate its effort, cost and risks. As demand of data for development and validation increases, simulations too must correspondingly be scaled. Variation of simulation parameters affords simulation designers control over the scope of how a simulation is scaled— they can chose a balance between target distribution of simulation variants and the degree of randomness— thereby achieving both the volume and diversity of synthetic data. This paper proposes logical scenarios as basis for simulation variation. Scenarios are formal human-readable scripts of simulations and test drives used within the automotive industry. They are defined at different abstraction levels, one of which is the logical scenario as a parameterized simulation model with description for parameters instead of concrete values. This contribution proposes methodologies to model the parameter descriptions in a modular fashion with parameter ranges, probability distributions and inter-relations. A randomization engine is introduced based on Markov chain Monte-Carlo methods to efficiently sample the modeled space. The result is a variety of simulation-independent concrete scenarios that follow the formal scenario specification. (More)

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Paper citation in several formats:
Maqbool, O. and Roßmann, J. (2022). Formal Scenario-driven Logical Spaces for Randomized Synthetic Data Generation. In Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development - MODELSWARD; ISBN 978-989-758-550-0; ISSN 2184-4348, SciTePress, pages 203-210. DOI: 10.5220/0010816400003119

@conference{modelsward22,
author={Osama Maqbool. and Jürgen Roßmann.},
title={Formal Scenario-driven Logical Spaces for Randomized Synthetic Data Generation},
booktitle={Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development - MODELSWARD},
year={2022},
pages={203-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010816400003119},
isbn={978-989-758-550-0},
issn={2184-4348},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development - MODELSWARD
TI - Formal Scenario-driven Logical Spaces for Randomized Synthetic Data Generation
SN - 978-989-758-550-0
IS - 2184-4348
AU - Maqbool, O.
AU - Roßmann, J.
PY - 2022
SP - 203
EP - 210
DO - 10.5220/0010816400003119
PB - SciTePress

- Science and Technology Publications, Lda.
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