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Youcef SKLAB edited this page Aug 22, 2019 · 42 revisions

Built-in Species (Under Construction)

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Agent Built-in

As described in the presentation of GAML, the species hierarchy derives from a single built-in species called agent. All its components (attributes, actions) will then be inherited by all direct or indirect children species (including model and experiment), with the exception of species that explicitly mention use_minimal_agents: true as a facet, which inherit from a stripped-down version of agent.

agent attributes

agent defines several attributes, which form the minimal set of knowledge any agent will have in a model. *

agent actions

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It is possible to use in the models a set of built-in agents. These agents allow to directly use some advance features like clustering, multi-criteria analysis, etc. The creation of these agents are similar as for other kinds of agents:

create species: my_built_in_agent returns: the_agent;

So, for instance, to be able to use clustering techniques in the model:

create cluster_builder returns: clusterer;

Table of Contents

agent, AgentDB, base_edge, experiment, graph_edge, graph_node, physical_world,

agent

Variables

  • host (-29): Returns the agent that hosts the population of the receiver agent
  • location (point): Returns the location of the agent
  • name (string): Returns the name of the agent (not necessarily unique in its population)
  • peers (list): Returns the population of agents of the same species, in the same host, minus the receiver agent
  • shape (geometry): Returns the shape of the receiver agent

Actions

_init_

  • returns: unknown

_step_

  • returns: unknown

AgentDB

Variables

  • agents (list): Returns the list of agents for the population(s) of which the receiver agent is a direct or undirect host
  • members (list): Returns the list of agents for the population(s) of which the receiver agent is a direct host

Actions

close

  • returns: unknown

connect

  • returns: unknown
  • params (map): Connection parameters

executeUpdate

  • returns: int
  • updateComm (string): SQL commands such as Create, Update, Delete, Drop with question mark
  • values (list): List of values that are used to replace question mark

getParameter

  • returns: unknown

insert

  • returns: int
  • into (string): Table name
  • columns (list): List of column name of table
  • values (list): List of values that are used to insert into table. Columns and values must have same size

isConnected

  • returns: bool

select

  • returns: list
  • select (string): select string
  • values (list): List of values that are used to replace question marks

setParameter

  • returns: unknown
  • params (map): Connection parameters

testConnection

  • returns: bool
  • params (map): Connection parameters

timeStamp

  • returns: float

base_edge

Variables

  • source (agent): The source agent of this edge
  • target (agent): The target agent of this edge

Actions

experiment

Experiments that declare a graphical user interface

Variables

  • minimum_cycle_duration (float): The minimum duration (in seconds) a simulation cycle should last. Default is 0. Units can be used to pass values smaller than a second (for instance '10 °msec')
  • model_path (string): Contains the absolute path to the folder in which the current model is located
  • project_path (string): Contains the absolute path to the project in which the current model is located
  • rng (string): The random number generator to use for this simulation. Three different ones are at the disposal of the modeler: mersenne represents the default generator, based on the Mersenne-Twister algorithm. Very reliable; cellular is a cellular automaton based generator that should be a bit faster, but less reliable; and java invokes the standard Java generator
  • rng_usage (int): Returns the number of times the random number generator of the experiment has been drawn
  • seed (float): The seed of the random number generator
  • simulation (-27): contains a reference to the current simulation being run by this experiment
  • simulations (list): contains the list of currently running simulations
  • warnings (boolean): The value of the preference 'Consider warnings as errors'
  • workspace_path (string): Contains the absolute path to the workspace of GAMA

Actions

compact_memory

Forces a 'garbage collect' of the unused objects in GAMA
  • returns: unknown

update_outputs

Forces all outputs to refresh, optionally recomputing their values
  • returns: unknown
  • recompute (boolean): Whether or not to force the outputs to make a computation step

graph_edge

Variables

  • source (agent): The source agent of this edge
  • target (agent): The target agent of this edge

Actions

graph_node

Variables

  • my_graph (graph): A reference to the graph containing the agent

Actions

related_to

This operator should never be called
  • returns: bool
  • other (agent): The other agent

physical_world

The base species for agents that act as a 3D physical world

Variables

  • agents (list): The list of agents registered in this physical world
  • gravity (float): Define if the value for the gravity
  • use_gravity (boolean): Define if the physical world has a gravity or not

Actions

compute_forces

  • returns: unknown
  • step (float): allows to define the time step considered for the physical world agent. If not defined, the physical world agent will use the step global variable.

Model Built-in

The 'model' built-in species (Under Construction)

As described in the presentation of GAML, any model in GAMA is a species (introduced by the keyword global) which directly inherits from an abstract species called model. This abstract species (sub-species of agent) defines several attributes and actions that can then be used in any global section of any model.

model attributes

model defines several attributes, which, in addition to the attributes inherited from agent, form the minimal set of knowledge a model can manipulate. *

model actions

Experiment Built-in

As described in the presentation of GAML, any experiment attached to a model is a species (introduced by the keyword experiment which directly or indirectly inherits from an abstract species called experiment itself. This abstract species (sub-species of agent) defines several attributes and actions that can then be used in any experiment.

experiment attributes

experiment defines several attributes, which, in addition to the attributes inherited from agent, form the minimal set of knowledge any experiment will have access to.

experiment actions

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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