Abstract
In this paper, we introduce a new method called SPSC (Simulation, Partitioning, Selection, Cloning) to estimate efficiently the probability of possible solutions in stochastic simulations. This method can be applied to any type of simulation, however it is particularly suitable for multi-agent-based simulations (MABS). Therefore, its performance is evaluated on a well-known MABS and compared to the classical approach, i.e., Monte Carlo.
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Acknowledgement
This work is partly funded by the ELSAT2020 project, which is co-financed by the European Union with the European Regional Development Fund, the French state and the Hauts de France Region Council.
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Huang, YL., Morvan, G., Pichon, F., Mercier, D. (2019). SPSC: A New Execution Policy for Exploring Discrete-Time Stochastic Simulations. In: Baldoni, M., Dastani, M., Liao, B., Sakurai, Y., Zalila Wenkstern, R. (eds) PRIMA 2019: Principles and Practice of Multi-Agent Systems. PRIMA 2019. Lecture Notes in Computer Science(), vol 11873. Springer, Cham. https://doi.org/10.1007/978-3-030-33792-6_42
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DOI: https://doi.org/10.1007/978-3-030-33792-6_42
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