Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-33792-6_42 | PRINCIPLES AND PRACTICE OF MULTI-AGENT SYSTEMS (PRIMA 2019) |
Keywords | Field | DocType |
Stochastic simulation, Multi-agent-based simulation, Solution space exploration | Computer science,Discrete time and continuous time,Distributed computing | Conference |
Volume | ISSN | Citations |
11873 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Lin Huang | 1 | 5 | 2.51 |
| Gildas Morvan | 2 | 31 | 7.10 |
| Frédéric Pichon | 3 | 84 | 13.14 |
| David Mercier | 4 | 0 | 0.34 |