Abstract
Due to their immense complexity, large-scale multiagent systems are often unamenable to exhaustive formal verification. Statistical approaches that focus on the verification of individual traces can provide an interesting alternative. However, due to its focus on finite execution paths, trace-based verification is inherently limited to certain types of correctness properties. We show how, by combining sampling with the idea of trace fragmentation, statistical model checking can be used to answer interesting quantitative correctness properties about multiagent systems on different observational levels. We illustrate the idea with a simple case study from the area of swarm robotics.
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Notes
- 1.
For simplicity, we omit the environment in our formal description.
- 2.
For simplicity, we ignore some of the intricate semantic issues of LTL in the presence of finite traces. For more information, please refer to the literature [3].
- 3.
We assume that agents are numbered from 1 to n and that the number of agents is fixed.
- 4.
The definition of functions for negative correlation and non-correlation, i.e. statistical independence, are omitted; they can be given accordingly.
- 5.
All experiments were conducted on a Viglen Genie Desktop PC with four Intel® Core™ i5 CPUs (3.2 GHz each), 3.7 GB of memory and Gentoo Linux (kernel version 3.10.25) as operating system, using the verification tool \( \texttt {MC} ^ \texttt {2} \texttt {MABS} \) [10]. Results are based on experiments involving 100 replications of the given model.
- 6.
For clarity, we abbreviate states with their capitalised first letters in all subsequent tables.
- 7.
For space limitation, the states are abbreviated with lower-case letters, e.g. s for searching.
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Herd, B., Miles, S., McBurney, P., Luck, M. (2015). Quantitative Analysis of Multiagent Systems Through Statistical Model Checking. In: Baldoni, M., Baresi, L., Dastani, M. (eds) Engineering Multi-Agent Systems. EMAS 2015. Lecture Notes in Computer Science(), vol 9318. Springer, Cham. https://doi.org/10.1007/978-3-319-26184-3_7
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