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Explainable Artificial Intelligence for Simulation Models

Published: 24 June 2024 Publication History

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

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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.
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Andrew J Collins, Tyann Thomas, and Gayane Grigoryan. 2019. Monte Carlo simulation of hedonic games. In MODSIM World 2019 Conference, Norfolk, VA, USA.
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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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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.
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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.
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Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017).
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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.
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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.
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Jayadev Misra. 1986. Distributed discrete-event simulation. ACM Computing Surveys (CSUR) 18, 1 (1986), 39–65.
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Cited By

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  • (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

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cover image ACM Conferences
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

New York, NY, United States

Publication History

Published: 24 June 2024

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Author Tags

  1. Explainable artificial intelligence
  2. agent-based models
  3. discrete event simulation
  4. simulation models
  5. system dynamics

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Cited By

View all
  • (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

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