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Alexis Drogoul edited this page Apr 17, 2023 · 121 revisions

GAMA

GAMA is an easy-to-use open source modeling and simulation environment for creating spatially explicit agent-based simulations. It has been developed to be used in any application domain: urban mobility, climate change adaptation, epidemiology, disaster evacuation strategy design, urban planning, are some of the application domains in which GAMA users are involved and for which they create models.

The generality of the agent-based approach advocated by GAMA is accompanied by a high degree of openness, which is manifested, for example, in the development of plugins designed to meet specific needs, or by the possibility of calling GAMA from other software or languages (such as R or Python). This openness allows the more than 2000 users of GAMA to use it for a wide variety of purposes: scientific simulation, scenario exploration and visualization, negotiation support, serious games, mediation or communication tools, the possibilities are endless!

The latest version of GAMA, labeled 1.9.1, can be freely downloaded or built from source, and comes with hundreds of templates, tutorials, and extensive online documentation.

Data-driven models

The relevance of agent-based models depends largely on the quality of the data on which they are built and the ease with which they can access it. GAMA offers the possibility to load and manipulate easily GIS (Geographic Information System) data in the models, in order to make them the environment of artificial agents. It is also possible to directly import and use directly in models a large number of data types, such as CSV files, Shapefiles, OSM data, grids, images, SVG files, but also 3D files, such as 3DS or OBJ. GAMA also offers models the possibility to connect directly to databases (UsingDatabase) and to use external tools and environments such as R.

GAML, a high-level, intuitive agent-based language

GAMA, although dedicated to providing a scientific approach to model building and exploration, was also developed to be used by non-computer scientists: it is possible to create a simulated world, declare agent species, assign behaviors to them and display them and their interactions in [less than 10 minutes] (https://www.youtube.com/watch?v=YGHw1LSzd-E).

GAML also offers all the power needed by advanced modelers: being an agent-oriented language coded in Java, it offers the possibility to build integrated models with [several modeling paradigms] (MultiParadigmModeling), to [explore their parameter space and calibrate them] (ExploringModels) and to run virtual experiments with powerful visualization capabilities, all without leaving the platform.

GAML can be learned easily by first following the step-by-step tutorial and then exploring the other tutorials and educational resources available on this site. Since 2007, the GAMA developers have also provided ongoing support via the active mailing list. Finally, in addition to this online support, training sessions for specialized audiences, on topics such as urban management, epidemiology, risk management, are also organized and delivered by GAMA developers and users.

Declarative user interface

The user interface for writing models and running experiments is one of the strong points of GAMA. The platform offers the possibility to have several displays for the same model, to add as many visual representations as necessary for the agents and thus to highlight the elements of interest in the simulations easily and nicely.

The 3D displays are provided with all the necessary support for realistic rendering. A rich set of instructions makes it easy to define graphics for more dashboard-like presentations.

During simulations, interactive features can be made available to inspect the population of agents, define user-controlled action panels, or interactions with the displays and external devices. GAMA also includes specific modules and plugins to handle the interactivity with the users through networks, handhelds, and other remote devices.

Declarative User Interface

Documentation

Beyond these features, GAMA also offers:

  • A large and extensible library of primitives (agent's movement, communication, mathematical functions, graphical features, ...)
  • A cross-platform reproducibility of experiments and simulations
  • A complete set of batch tools, allowing for a systematic or "intelligent" exploration of models parameters spaces

and much more !

All the features of GAMA are documented online on this wiki. It is organized around a few central activities (installing GAMA, writing models, running experiments, developing new extensions to the platform) and provides complete references on both the GAML language, the platform itself, the scientific aspects behind GAMA (with a complete bibliography), and also all the communication around it, notably videos here and here. Several tutorials are also provided in the documentation in order to minimize the learning curve, allowing users to build, step by step, the models corresponding to these tutorials, which are of course shipped with the platform. The documentation can be accessed from the sidebar of this page. A good starting point for new users is the installation page.

Source Code

GAMA can be downloaded as a regular application or built from source, which is necessary if you want to contribute to the platform. The source code is available from this GITHub repository:

https://github.com/gama-platform/gama

Which you can also browse from here. It is, in any case, recommended to follow the instructions on this page in order to build GAMA from source.

Development Team

GAMA is developed by several teams under the umbrella of the IRD/SU international research unit UMMISCO:

Developers

GAMA is being designed, developed and maintained by an active group of researchers coming from these different institutions. Please find below a short introduction to each of them and a summary of their contributions to the platform:

  • Alexis Drogoul, Senior Researcher at the IRD, member of the UMMISCO International Research Unit. Mostly working on agent-based modeling and simulation. Has contributed and still contributes to the original design of the platform, including the GAML language (from the meta-model to the editor) and simulation facilities like Java2D displays.
  • Patrick Taillandier, Senior Researcher at INRAE, member of the MIAT Research Unit. Contributes since 2008 to the spatial and graph features (GIS integration, spatial operators), graphical modeling, human behavior modeling, model exploration and traffic simulation.
  • Benoit Gaudou, Associate Professor at the University Toulouse 1 Capitole, member of the IRIT CNRS Mixed Research Unit. Contributes since 2010 to documentation and unit test generation and coupling mathematical (ODE and PDE) and agent paradigms.
  • Arnaud Grignard, Computer Scientist at Université de Lyon as a Marie Curie Fellowship and research associate at the MIT Media Lab CityScience, software engineer and PhD fellow (PDI-MSC) at SU. Contributes since 2011 to the development of new features related to visualization, interaction, online analysis and tangible interfaces.
  • Huynh Quang Nghi, software engineering lecturer at CTU and PhD fellow (PDI-MSC) at SU. Contributes since 2012 to the development of new features related to GAML parser, coupling formalisms in EBM-ABM and ABM-ABM, GAMA server and co-modeling.
  • Truong Minh Thai, software engineering lecturer at CTU and PhD fellow (PRJ322-MOET) at IRIT-UT1. Contributes since 2012 to the development of new features related to data management and analysis.
  • Nicolas Marilleau, Researcher at the IRD, member of the UMMISCO International Research Unit and associate researcher at DISC team of FEMTO-ST institute. Contributes since 2010 to the development of headless mode and the high performance computing module.
  • Philippe Caillou, Associate professor at the University Paris Sud 11, member of the LRI and INRIA project-team TAO. Contributes since 2012 and actually working on charts, simulation analysis and BDI agents.
  • Vo Duc An, Post-doctoral Researcher, working on synthetic population generation in agent-based modelling, at the UMMISCO International Research Unit of the IRD. Has contributed to bringing the platform to the Eclipse RCP environment and to the development of several features (e.g., the FIPA-compliant agent communication capability, the multi-level architecture).
  • Truong Xuan Viet, software engineering lecturer at CTU and PhD fellow (PDI-MSC) at SU. Contributes since 2011 to the development of new features related to R caller, online GIS (OPENGIS: Web Map Service - WMS, Web Feature Services - WMS, Google map, etc).
  • Kevin Chapuis, Researcher at IRD and independent consultant. Mostly working on generation of synthetic populations and improvements to the batch experiments. Has contributed to the design and development of the genstar plugin, as well as the new batch architecture in GAMA 1.9.1.
  • Jean-Daniel.Zucker, Senior Researcher at IRD, member and director of UMMISCO. Mostly working on Machine Learning and also optimization using agent-based modeling and simulation. Has contributed to different models and advised different students on GAMA since its beginning.
  • Arthur Brugiere, Lead Technical Officer at UMMISCO Vietnam. In charge of the build tools and the architecture of GAMA repositories. Working mainly on multi-level capabilities inside GAMA.
  • Baptiste Lesquoy, International volunteer at UMMISCO Vietnam. In charge of the co-development of GAMA server. Working mainly on coupling GAMA with various environments (Unreal Engine, Python, R, Matlab...).
  • Srirama Bhamidipati, Independent researcher. In charge of the development of the communication and documentation around GAMA, community manager of the GAMA Google Group.
  • Tri Nguyen Huu, researcher at IRD, member of UMMISCO. In charge of the development and maintenance of the maths plugin. Has contributed a number of library models on the coupling of equation-based modelling and agent-based modelling.

Citing GAMA

If you use GAMA in your research and want to cite it (in a paper, presentation, whatever), please use this reference:

Taillandier, P., Gaudou, B., Grignard, A.,Huynh, Q.-N., Marilleau, N., P. Caillou, P., Philippon, D., & Drogoul, A. (2019). Building, composing and experimenting complex spatial models with the GAMA platform. Geoinformatica, (2019), 23 (2), pp. 299-322, [doi:10.1007/s10707-018-00339-6]

or you can choose to cite the website instead:

GAMA Platform website, http://gama-platform.org

A complete list of references (papers and PhD theses on or using GAMA) is available on the references page.

Contact Us

To get in touch with the GAMA developers team, please sign in for the [email protected] mailing list. If you wish to contribute to the platform, you might want, instead or in addition, to sign in for the [email protected] mailing list. On both lists, we generally answer quite quickly to requests.

Finally, to report bugs in GAMA or ask for a new feature, please refer to these instructions to do so.

Copyright Information

This is a free software (distributed under the GNU GPL v3 license), so you can have access to the code, edit it and redistribute it under the same terms. Independently of the licensing issues, if you plan on reusing part of our code, we would be glad to know it !

Acknowledgement

YourKit logo

YourKit supports open source projects with its full-featured Java Profiler. YourKit, LLC is the creator of YourKit Java Profiler and YourKit .NET Profiler, innovative and intelligent tools for profiling Java and .NET applications.

Creative Commons License
This page is licensed under a Creative Commons Attribution 4.0 International License.

Home

  1. What's new (Changelog)

Platform

  1. Installation and Launching
    1. Installation
    2. Launching GAMA
    3. Updating GAMA
    4. Installing Plugins
  2. Workspace, Projects and Models
    1. Navigating in the Workspace
    2. Changing Workspace
    3. Importing Models
  3. Editing Models
    1. GAML Editor (Generalities)
    2. GAML Editor Tools
    3. Validation of Models
  4. Running Experiments
    1. Launching Experiments
    2. Experiments User interface
    3. Controls of experiments
    4. Parameters view
    5. Inspectors and monitors
    6. Displays
    7. Batch Specific UI
    8. Errors View
  5. Running Headless
    1. Headless Batch
    2. Headless Server
    3. Headless Legacy
  6. Preferences
  7. Troubleshooting

Learn GAML step by step

  1. Introduction
    1. Start with GAML
    2. Organization of a Model
    3. Basic programming concepts in GAML
  2. Manipulate basic Species
  3. Global Species
    1. Regular Species
    2. Defining Actions and Behaviors
    3. Interaction between Agents
    4. Attaching Skills
    5. Inheritance
  4. Defining Advanced Species
    1. Grid Species
    2. Graph Species
    3. Mirror Species
    4. Multi-Level Architecture
  5. Defining GUI Experiment
    1. Defining Parameters
    2. Defining Displays Generalities
    3. Defining 3D Displays
    4. Defining Charts
    5. Defining Monitors and Inspectors
    6. Defining Export files
    7. Defining User Interaction
  6. Exploring Models
    1. Run Several Simulations
    2. Batch Experiments
    3. Exploration Methods
  7. Optimizing Model Section
    1. Runtime Concepts
    2. Optimizing Models
  8. Multi-Paradigm Modeling
    1. Control Architecture
    2. Defining Differential Equations

Recipes

  1. Manipulate OSM Data
  2. Diffusion
  3. Using Database
  4. Using FIPA ACL
  5. Using BDI with BEN
  6. Using Driving Skill
  7. Manipulate dates
  8. Manipulate lights
  9. Using comodel
  10. Save and restore Simulations
  11. Using network
  12. Headless mode
  13. Using Headless
  14. Writing Unit Tests
  15. Ensure model's reproducibility
  16. Going further with extensions
    1. Calling R
    2. Using Graphical Editor
    3. Using Git from GAMA

GAML References

  1. Built-in Species
  2. Built-in Skills
  3. Built-in Architecture
  4. Statements
  5. Data Type
  6. File Type
  7. Expressions
    1. Literals
    2. Units and Constants
    3. Pseudo Variables
    4. Variables And Attributes
    5. Operators [A-A]
    6. Operators [B-C]
    7. Operators [D-H]
    8. Operators [I-M]
    9. Operators [N-R]
    10. Operators [S-Z]
  8. Exhaustive list of GAMA Keywords

Developing GAMA

  1. Installing the GIT version
  2. Developing Extensions
    1. Developing Plugins
    2. Developing Skills
    3. Developing Statements
    4. Developing Operators
    5. Developing Types
    6. Developing Species
    7. Developing Control Architectures
    8. Index of annotations
  3. Introduction to GAMA Java API
    1. Architecture of GAMA
    2. IScope
  4. Using GAMA flags
  5. Creating a release of GAMA
  6. Documentation generation

Tutorials

  1. Predator Prey
  2. Road Traffic
  3. 3D Tutorial
  4. Incremental Model
  5. Luneray's flu
  6. BDI Agents

Community

  1. Team
  2. Projects using GAMA
  3. Scientific References
  4. Training Sessions

Versions of GAMA

Resources

  1. Videos
  2. Conferences
  3. Code Examples
  4. Pedagogical materials
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