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2021 | OriginalPaper | Chapter

21. Towards the Identification and Visualization of Causal Events to Support the Analysis of Closed-Loop Combat Simulations

Authors : Dion Grieger, Martin Wong, Marco Tamassia, Luis Torres, Antonio Giardina, Rajesh Vasa, Kon Mouzakis

Published in: Data and Decision Sciences in Action 2

Publisher: Springer International Publishing

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Abstract

Analysis of output data from closed-loop combat simulations can provide insights into the relationships between model inputs and outputs. However, analyses that only consider end-of-run output data may not be sufficient to explain why those relationships exist. In the case of stochastic models, where multiple replications of the same scenario are conducted, the presence of outliers and multi-modal results also needs to be accounted for. In this paper, we use a military case study to explore a range of techniques to interrogate the intra-run event data (i.e. trace logs) generated by a combat simulation in order to help address these two issues. Cumulative event plots and geo-spatial visualization techniques which also incorporate the temporal aspects of the simulation appear best suited to explain the presence of outlier replications and multi-modal results. Exploratory work using hierarchical clustering techniques and temporal decision trees provide a promising step towards better explaining causal events within the combat simulation data.

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Metadata
Title
Towards the Identification and Visualization of Causal Events to Support the Analysis of Closed-Loop Combat Simulations
Authors
Dion Grieger
Martin Wong
Marco Tamassia
Luis Torres
Antonio Giardina
Rajesh Vasa
Kon Mouzakis
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-60135-5_21