MC2MABS: A Monte Carlo Model Checker for Multiagent-based Simulations

Benjamin Christoph Herd; Simon Miles; Peter John McBurney; Michael Mordechai Luck

doi:10.1007/978-3-319-31447-1_3

MC2MABS: A Monte Carlo Model Checker for Multiagent-based Simulations

Benjamin Christoph Herd, Simon Miles, Peter John McBurney, Michael Mordechai Luck

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

7 Citations (Scopus)

173 Downloads (Pure)

Abstract

Agent-based simulation has shown great success for the study of complex adaptive systems and could in many areas show advantages over traditional analytical methods. Due to their internal complexity, however, agent-based simulations are notoriously difficult to verify and validate. This paper presents MC²MABS, a Monte Carlo Model Checker for Multiagent-Based Simulations. It incorporates the idea of statistical runtime verification, a combination of statistical model checking and runtime verification, and is tailored to the approximate verification of complex agent-based simulations. We provide a description of the underlying theory together with design decisions, an architectural overview, and implementation details. The performance of MC²MABS in terms of both runtime consumption and memory allocation is evaluated against a set of example properties.

Original language	English
Title of host publication	Multi-Agent-Based Simulation XVI
Subtitle of host publication	Proceedings of the Sixteenth International Workshop on Multi-Agent-Based Simulation (MABS 2015)
Publisher	Springer
Pages	37-54
Number of pages	18
Volume	9586
DOIs	https://doi.org/10.1007/978-3-319-31447-1_3
Publication status	Published - 2016
Event	16th International Workshop on Multi-Agent-Based Simulation, MABS 2015 - Istanbul, Turkey Duration: 5 May 2015 → 5 May 2015

Publication series

Name	Lecture Notes in Computer Science

Conference

Conference	16th International Workshop on Multi-Agent-Based Simulation, MABS 2015
Country/Territory	Turkey
City	Istanbul
Period	5/05/2015 → 5/05/2015

Keywords

Agent-based simulation
Formal methods
Testing
Verification

Access to Document

10.1007/978-3-319-31447-1_3

MC2MABS: A Monte Carlo_HERD_Published 2015_GREEN AAMAccepted author manuscript, 312 KB

Cite this

Herd, B. C., Miles, S., McBurney, P. J., & Luck, M. M. (2016). MC2MABS: A Monte Carlo Model Checker for Multiagent-based Simulations. In Multi-Agent-Based Simulation XVI: Proceedings of the Sixteenth International Workshop on Multi-Agent-Based Simulation (MABS 2015) (Vol. 9586, pp. 37-54). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-31447-1_3

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Herd, BC , Miles, S , McBurney, PJ & Luck, MM 2016, MC2MABS: A Monte Carlo Model Checker for Multiagent-based Simulations. in Multi-Agent-Based Simulation XVI: Proceedings of the Sixteenth International Workshop on Multi-Agent-Based Simulation (MABS 2015). vol. 9586, Lecture Notes in Computer Science, Springer, pp. 37-54, 16th International Workshop on Multi-Agent-Based Simulation, MABS 2015, Istanbul, Turkey, 5/05/2015. https://doi.org/10.1007/978-3-319-31447-1_3

MC2MABS: A Monte Carlo Model Checker for Multiagent-based Simulations. / Herd, Benjamin Christoph ; Miles, Simon ; McBurney, Peter John et al.
Multi-Agent-Based Simulation XVI: Proceedings of the Sixteenth International Workshop on Multi-Agent-Based Simulation (MABS 2015). Vol. 9586 Springer, 2016. p. 37-54 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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AB - Agent-based simulation has shown great success for the study of complex adaptive systems and could in many areas show advantages over traditional analytical methods. Due to their internal complexity, however, agent-based simulations are notoriously difficult to verify and validate. This paper presents MC2MABS, a Monte Carlo Model Checker for Multiagent-Based Simulations. It incorporates the idea of statistical runtime verification, a combination of statistical model checking and runtime verification, and is tailored to the approximate verification of complex agent-based simulations. We provide a description of the underlying theory together with design decisions, an architectural overview, and implementation details. The performance of MC2MABS in terms of both runtime consumption and memory allocation is evaluated against a set of example properties.

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KW - Formal methods

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MC2MABS: A Monte Carlo Model Checker for Multiagent-based Simulations

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