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Baptiste Lesquoy edited this page May 13, 2022 · 103 revisions

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  1. Introduction

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 Legacy
    2. Headless Batch
    3. Headless Server
  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. Writing Unit Tests
  14. 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

Tutorials

1.Pedagogical materials

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

Developing GAMA

  1. Introduction to GAMA Java API
    1. Installing the GIT version
    2. Architecture of GAMA
    3. IScope
  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. Creating a release of GAMA
  4. Documentation generation

Projects using GAMA

  1. Scientific References
  2. Training Session
  3. Events

Historical versions

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GAMA Platform

GAMA is an open-source modeling and simulation development environment for building spatially explicit agent-based simulations. It has been developed with a very general approach and can be used in any application domain. ). Some additional plugins have been developed to fit particular needs. The source code is available from the dedicated Github repository.

Its latest version, 1.8.2, can be freely downloaded or built from source, and comes preloaded with hundreds of models, tutorials and a complete on-line documentation.

Large data-driven models

GAMA provides, since its creation, the possibility to load and manipulate GIS (Geographic Information System) data in the models. One can also import and directly use a large number of data types, such as CSV files, shapefiles, OSM (open street map data), grid, images, SVG, but also 3D files, such as 3DS or OBJ. It also provides models with the possibility to directly connect to databases and use external tools and environments such as R.

Data-driven models

High-level and intuitive agent-based language

Thanks to GAML, its high-level and intuitive language, GAMA has been developed to be used by non-computer scientists: one can actually create a simulated world, declare species of agents, provide them with behaviors, and display them and their interactions in less than 10 minutes. GAML also offers all the power needed by advanced modellers: being an agent-oriented language coded in Java, it provides the possibility to build integrated models with several paradigms of modeling, to explore their parameters space and calibrate them and to run virtual experiments, all of these without leaving the platform.

GAML can be learnt easily by following first the step by step tutorial and then exploring the other tutorials and pedagogical resources available throughout this site. Since 2007, the developers behind GAMA also provide a continuous support through the active mailing list. Finally, in addition to this online support, training sessions for specialised audiences, on topics such as "urban management", "epidemiology", "risk management" are also organised and delivered by GAMA developers and users.

Declarative user interface

The user interface for both writing models and running experiments is one of the strongest points of GAMA. The platform indeed provides the possibility to have multiple displays for the same model, add as many visual representations as needed for the agents and therefore highlight the elements of interest in the simulations easily and beautifully. Advanced 3D displays are provided with all the support required for realistic renderings. Of course, dedicated statements allow to easily define charts 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.

Declarative User Interface


Development Team

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

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.

Acknowledgement

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