PT Unknown
AU Feldkamp, N
   Straßburger, S
AU ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation (SIGSIM PADS) (Orlando, Fla.) : 2023.06.21-23
TI From explainable AI to explainable simulation: using machine learning and XAI to understand system robustness
SE ACM SIGSIM-PADS 2023: proceedings of the 2023 ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation, June 21-23, 2023, Orlando, Fla
PD June
PY 2023
PU Association for Computing Machinery
DI 10.1145/3573900.3591114
WP https://www.db-thueringen.de/receive/dbt_mods_00062116
LA en
DE machine learning; deep learning; robustness optimization; simulation; explainable AI; XAI
SN 979-8-4007-0030-9
AB Evaluating robustness is an important goal in simulation-based analysis. Robustness is achieved when the controllable factors of a system are adjusted in such a way that any possible variance in uncontrollable factors (noise) has minimal impact on the variance of the desired output. The optimization of system robustness using simulation is a dedicated and well-established research direction. However, once a simulation model is available, there is a lot of potential to learn more about the inherent relationships in the system, especially regarding its robustness. Data farming offers the possibility to explore large design spaces using smart experiment design, high performance computing, automated analysis, and interactive visualization. Sophisticated machine learning methods excel at recognizing and modelling the relation between large amounts of simulation input and output data. However, investigating and analyzing this modelled relationship can be very difficult, since most modern machine learning methods like neural networks or random forests are opaque black boxes. Explainable Artificial Intelligence (XAI) can help to peak into this black box, helping us to explore and learn about relations between simulation input and output. In this paper, we introduce a concept for using Data Farming, machine learning and XAI to investigate and understand system robustness of a given simulation model.
PI New York, NY
ER