MASON

MASON: A Multiagent Simulation Environment. MASON is a fast, easily extensible, discrete-event multi-agent simulation toolkit in Java, designed to serve as the basis for a wide range of multi-agent simulation tasks ranging from swarm robotics to machine learning to social complexity environments. MASON carefully delineates between model and visualization, allowing models to be dynamically detached from or attached to visualizers, and to change platforms mid-run. This paper describes the MASON system, its motivation, and its basic architectural design. It then compares MASON to related multi-agent libraries in the public domain, and discusses six applications of the system built over the past year which suggest its breadth of utility.


References in zbMATH (referenced in 22 articles )

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  1. Chathika Gunaratne, Ivan Garibay: NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces (2021) not zbMATH
  2. Ali R. Vahdati: Agents.jl: agent-based modeling framework in Julia (2019) not zbMATH
  3. Marcos Cardinot; Colm O’Riordan; Josephine Griffith; Matjaž Perc: Evoplex: A platform for agent-based modeling on networks (2019) not zbMATH
  4. R. Zupko, M. Rouleau: ForestSim: Spatially explicit agent-based modeling of non-industrial forest owner policies (2019) not zbMATH
  5. Sheraton, M. V.; Melnikov, V. R.; Sloot, P. M. A.: Prediction and quantification of bacterial biofilm detachment using Glazier-Graner-Hogeweg method based model simulations (2019)
  6. Jordan Jalving, Yankai Cao, Victor M. Zavala: Graph-Based Modeling and Simulation of Complex Systems (2018) arXiv
  7. Smaldino, Paul E.; Janssen, Marco A.; Hillis, Vicken; Bednar, Jenna: Adoption as a social marker: innovation diffusion with outgroup aversion (2017)
  8. Iori, Giulia; Mantegna, Rosario N.; Marotta, Luca; Miccichè, Salvatore; Porter, James; Tumminello, Michele: Networked relationships in the e-MID interbank market: a trading model with memory (2015)
  9. Nunes, Davide; Antunes, Luis: Modelling structured societies: a multi-relational approach to context permeability (2015)
  10. Schank, Jeffrey C.; Smaldino, Paul E.; Miller, Matt L.: Evolution of fairness in the dictator game by multilevel selection (2015)
  11. Aznar, F.; Sempere, M.; Pujol, M.; Rizo, R.; Pujol, M. J.: Modelling oil-spill detection with swarm drones (2014)
  12. Aydemir, Fatma Başak; Günay, Akın; Öztoprak, Figen; İlker Birbil, Ş.; Yolum, Pınar: Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies (2013)
  13. Bing, Li; Jie, Li; Kewei, Huang: Modeling and flocking consensus analysis for large-scale UAV swarms (2013) ioport
  14. Serrano, Emilio; Botia, Juan: Validating ambient intelligence based ubiquitous computing systems by means of artificial societies (2013) ioport
  15. Read, Mark; Andrews, Paul S.; Timmis, Jon; Kumar, Vipin: Techniques for grounding agent-based simulations in the real domain: a case study in experimental autoimmune encephalomyelitis (2012)
  16. Smaldino, Paul E.; Schank, Jeffrey C.: Movement patterns, social dynamics, and the evolution of cooperation (2012)
  17. da Silva, Paulo Salem; de Melo, Ana C. V.: A formal environment model for multi-agent systems (2011)
  18. Makowsky, Michael D.: A theory of liberal churches (2011)
  19. Mei, Shan; Sloot, P. M. A.; Quax, Rick; Zhu, Yifan; Wang, Weiping: Complex agent networks explaining the HIV epidemic among homosexual men in Amsterdam (2010)
  20. Zappala, Julian; Logan, Brian: Effects of resource availability on consensus decision making in primates (2010) ioport

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