Short Duration Aggregate Statistical Model Checking for Multi-Agent Systems

Yenda, R. and Panduranga Rao, M. V. (2021) Short Duration Aggregate Statistical Model Checking for Multi-Agent Systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12568. pp. 420-427. ISSN 0302-9743

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For analysing large and complex systems, Statistical Model Checking has proved to be an attractive alternative to the more expensive numerical model checking approaches. Statistical Model Checking involves Monte Carlo sampling of execution traces of the system. Stochastic multi-agent systems with very large agent populations add significant simulation overheads that dominate the model checking complexity. This offsets some of the advantage in terms of the speed that statistical model checking offers. To mitigate these simulation overheads, we explore an approach based on sampling agent populations in addition to the Monte Carlo sampling of execution traces. We argue that this approach is particularly useful for aggregate queries on Multi-Agent systems that are also restricted in the time horizon–for example, bounded until operators in probabilistic temporal languages. We show that this can result in significant improvement in running times at the expense of only a marginal loss in accuracy and provide empirical evidence for this.

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IITH Creators:
IITH CreatorsORCiD
Panduranga Rao, M VUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Aggregate queries; Execution trace; Marginal loss; Monte Carlo sampling; Short durations; Statistical model checking; Stochastic multi-agent systems; Temporal language Engineering main heading
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 15 Jul 2021 04:35
Last Modified: 18 Feb 2022 08:54
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