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Explainable Artificial Intelligence for Simulation Models

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Gayane GrigoryanAuthors Info & Claims

SIGSIM-PADS '24: Proceedings of the 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

Pages 59 - 60

https://doi.org/10.1145/3615979.3662148

Published: 24 June 2024 Publication History

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Abstract

Simulation models, including discrete event simulation, agent-based models, and system dynamics, are employed to study various scenarios and behaviors. However, understanding these models can be particularly challenging because they depend on varying inputs and parameters. This study proposes the use of existing and new explainable artificial intelligence techniques to enhance the understanding of these simulation models.

References

[1]

Bernd Blasius, Lars Rudolf, Guntram Weithoff, Ursula Gaedke, and Gregor F Fussmann. 2020. Long-Term Cyclic Persistence in an Experimental Predator–Prey System. Nature 577, 7789 (2020), 226–230.

Google Scholar

[2]

Andrew J Collins and Gayane Grigoryan. 2024. ABMSCORE: a heuristic algorithm for forming strategic coalitions in agent-based simulation. Journal of Simulation (2024), 1–25.

Google Scholar

[3]

Andrew J Collins, Tyann Thomas, and Gayane Grigoryan. 2019. Monte Carlo simulation of hedonic games. In MODSIM World 2019 Conference, Norfolk, VA, USA.

Google Scholar

[4]

Gayane Grigoryan. 2022. Explainable Artificial Intelligence: Requirements for Explainability. In Proceedings of the 2022 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. 27–28.

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Google Scholar

[5]

Gayane Grigoryan and Andrew J Collins. 2021. Game theory for systems engineering: a survey. International Journal of System of Systems Engineering 11, 2 (2021), 121–158.

Crossref

Google Scholar

[6]

Gayane Grigoryan and Andrew J Collins. 2023. Feature Importance for Uncertainty Quantification In Agent-Based Modeling. In 2023 Winter Simulation Conference (WSC). IEEE, 233–242.

Google Scholar

[7]

Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017).

Google Scholar

[8]

Christopher J Lynch, Ross Gore, Andrew J Collins, T Steven Cotter, Gayane Grigoryan, and James F Leathrum. 2021. Increased need for data analytics education in support of verification and validation. In 2021 Winter Simulation Conference (WSC). IEEE, 1–12.

Crossref

Google Scholar

[9]

Charles M Macal and Michael J North. 2009. Agent-Based Modeling and Simulation. In Proceedings of the 2009 Winter Simulation Conference, M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls (Eds.). Institute of Electrical and Electronics Engineers, Inc., Piscataway, New Jersey, 86–98.

Crossref

Google Scholar

[10]

Jayadev Misra. 1986. Distributed discrete-event simulation. ACM Computing Surveys (CSUR) 18, 1 (1986), 39–65.

Digital Library

Google Scholar

[11]

Daniele Vernon-Bido, Gayane Grigoryan, Hamdi Kavak, and Jose Padilla. 2018. Assessing the impact of cyberloafing on cyber risk. In Proceedings of the Annual Simulation Symposium. 1–9.

Digital Library

Google Scholar

Cited By

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Liu JLiu SLai RLin Q(2024)Improved Banzhaf Value Based on Participant’s Triangular Fuzzy Number-Weighted Excess Contributions and Its Application in Manufacturing Supply Chain CoalitionsSymmetry10.3390/sym1612159316:12(1593)Online publication date: 29-Nov-2024
https://doi.org/10.3390/sym16121593

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SIGSIM-PADS '24: Proceedings of the 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

June 2024

155 pages

ISBN:9798400703638

DOI:10.1145/3615979

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 24 June 2024

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SIGSIM

SIGSIM-PADS '24: 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

June 24 - 26, 2024

GA, Atlanta, USA

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Liu JLiu SLai RLin Q(2024)Improved Banzhaf Value Based on Participant’s Triangular Fuzzy Number-Weighted Excess Contributions and Its Application in Manufacturing Supply Chain CoalitionsSymmetry10.3390/sym1612159316:12(1593)Online publication date: 29-Nov-2024
https://doi.org/10.3390/sym16121593

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Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction

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