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Using Database Access

Database features of GAMA provide a set of actions on Database Management Systems (DBMS) and Multi-Dimensional Database for agents in GAMA. Database features are implemented in the irit.gaml.extensions.database plug-in with these features:

  • Agents can execute SQL queries (create, Insert, select, update, drop, delete) to various kinds of DBMS.
  • Agents can execute MDX (Multidimensional Expressions) queries to select multidimensional objects, such as cubes, and return multidimensional cellsets that contain the cube's data.

These features are implemented in two kinds of component: skills (SQLSKILL, MDXSKILL) and agent (AgentDB)

SQLSKILL and AgentDB provide almost the same features (a same set of actions on DBMS) but with certain slight differences:

  • An agent of species AgentDB will maintain a unique connection to the database during the whole simulation. The connection is thus initialized when the agent is created.
  • In contrast, an agent of a species with the SQLSKILL skill will open a connection each time he wants to execute a query. This means that each action will be composed of three running steps:
    • Make a database connection.
    • Execute SQL statement.
    • Close database connection.

An agent with the SQLSKILL spends lot of time to create/close the connection each time it needs to send a query; it saves the database connection (DBMS often limit the number of simultaneous connections). In contrast, an AgentDB agent only needs to establish one database connection and it can be used for any actions. Because it does not need to create and close database connection for each action: therefore, actions of AgentDB agents are executed faster than actions of SQLSKILL ones but we must pay a connection for each agent.

  • With an inheritance agent of species AgentDB or an agent of a species using SQLSKILL, we can query data from relational database for creating species, defining environment or analyzing or storing simulation results into RDBMS. On the other hand, an agent of species with MDXKILL supports the OLAP technology to query data from data marts (multidimensional database). The database features help us to have more flexibility in management of simulation models and analysis of simulation results.

Description

  • Plug-in: irit.gaml.extensions.database
  • Author: TRUONG Minh Thai, Frederic AMBLARD, Benoit GAUDOU, Christophe SIBERTIN-BLANC

Supported DBMS

The following DBMS are currently supported:

  • SQLite
  • MySQL Server
  • PostgreSQL Server
  • SQL Server
  • Mondrian OLAP Server
  • SQL Server Analysis Services

Note that, other DBMSs require a dedicated server to work while SQLite on only needs a file to be accessed. All the actions can be used independently from the chosen DBMS. Only the connection parameters are DBMS-dependent.

SQLSKILL

Define a species that uses the SQLSKILL skill

Example of declaration:

species toto skills: [SQLSKILL] {
	//insert your descriptions here
}

Agents with such a skill can use additional actions (defined in the skill)

Map of connection parameters for SQL

In the actions defined in the SQLSkill, a parameter containing the connection parameters is required. It is a map with the following key::value pairs:

Key Optional Description
dbtype No DBMS type value. Its value is a string. We must use "mysql" when we want to connect to a MySQL. That is the same for "postgres", "sqlite" or "sqlserver" (ignore case sensitive)
host Yes Host name or IP address of data server. It is absent when we work with SQlite.
port Yes Port of connection. It is not required when we work with SQLite.
database No Name of database. It is the file name including the path when we work with SQLite.
user Yes Username. It is not required when we work with SQLite.
passwd Yes Password. It is not required when we work with SQLite.
srid Yes srid (Spatial Reference Identifier) corresponds to a spatial reference system. This value is specified when GAMA connects to spatial database. If it is absent then GAMA uses spatial reference system defined in Preferences->External configuration.
Table 1: Connection parameter description

Example: Definitions of connection parameter

// POSTGRES connection parameter
map <string, string>  POSTGRES <- [
     'host'::'localhost',
     'dbtype'::'postgres',
     'database'::'BPH',
     'port'::'5433',
     'user'::'postgres',
     'passwd'::'abc'];

//SQLite
map <string, string>  SQLITE <- [
    'dbtype'::'sqlite',
    'database'::'../includes/meteo.db'];

// SQLSERVER connection parameter
map <string, string> SQLSERVER <- [
    'host'::'localhost',
    'dbtype'::'sqlserver',
    'database'::'BPH',
    'port'::'1433',
    'user'::'sa',
    'passwd'::'abc'];

// MySQL connection parameter
map <string, string>  MySQL <- [
    'host'::'localhost',
    'dbtype'::'MySQL',
    'database'::'', // it may be a null string
    'port'::'3306',
    'user'::'root',
    'passwd'::'abc'];

Test a connection to database

Syntax:

testConnection (params: connection_parameter) The action tests the connection to a given database.

  • Return: boolean. It is:
    • true: the agent can connect to the DBMS (to the given Database with given name and password)
    • false: the agent cannot connect
  • Arguments:
    • params: (type = map) map of connection parameters
  • Exceptions: GamaRuntimeException

Example: Check a connection to MySQL

if (self testConnection(params:MySQL)){
	write "Connection is OK" ;
}else{
	write "Connection is false" ;
}

Select data from database

Syntax:

select (param: connection_parameter, select: selection_string,values: value_list) The action creates a connection to a DBMS and executes the select statement. If the connection or selection fails then it throws a GamaRuntimeException.

  • Return: list < list >. If the selection succeeds, it returns a list with three elements:
    • The first element is a list of column name.
    • The second element is a list of column type.
    • The third element is a data set.
  • Arguments:
    • params: (type = map) map containing the connection parameters
    • select: (type = string) select string. The selection string can contain question marks.
    • values: List of values that are used to replace question marks in appropriate. This is an optional parameter.
  • Exceptions: GamaRuntimeException

Example: select data from table points

map <string, string>   PARAMS <- ['dbtype'::'sqlite', 'database'::'../includes/meteo.db'];
list<list> t <- list<list> (self select(params:PARAMS, 
		                 select:"SELECT * FROM points ;"));

Example: select data from table point with question marks from table points

map <string, string>   PARAMS <- ['dbtype'::'sqlite', 'database'::'../includes/meteo.db'];
list<list> t <- list<list> (self select(params: PARAMS, 
                                           select: "SELECT temp_min FROM points where (day>? and day<?);"
                                           values: [10,20] ));

Insert data into database

Syntax:

_insert (param: connection_parameter, into: table_name, columns: column_list, values: value`_list)The action creates a connection to a DBMS and executes the insert statement. If the connection or insertion fails then it throws a_GamaRuntimeException.

  • Return: int

If the insertion succeeds, it returns a number of records inserted by the insert.

  • Arguments: *params: (type = map) map containing the connection parameters. *into: (type = string) table name. *columns: (type=list) list of column names of table. It is an optional argument. If it is not applicable then all columns of table are selected. *values: (type=list) list of values that are used to insert into table corresponding to columns. Hence the columns and values must have same size.
  • Exceptions:_GamaRuntimeException

Example: Insert data into table registration

map<string, string> PARAMS <- ['dbtype'::'sqlite', 'database'::'../../includes/Student.db'];

do insert (params: PARAMS, 
               into: "registration", 
               values: [102, 'Mahnaz', 'Fatma', 25]);

do insert (params: PARAMS, 
                into: "registration", 
                columns: ["id", "first", "last"], 
                values: [103, 'Zaid tim', 'Kha']);

int n <- insert (params: PARAMS, 
                        into: "registration", 
                       columns: ["id", "first", "last"], 
                       values: [104, 'Bill', 'Clark']);

Execution update commands

Syntax:

executeUpdate (param: connection_parameter, updateComm: table_name, values: value_list) The action executeUpdate executes an update command (create/insert/delete/drop) by using the current database connection of the agent. If the database connection does not exist or the update command fails then it throws a GamaRuntimeException. Otherwise it returns an integer value.

  • Return: int. If the insertion succeeds, it returns a number of records inserted by the insert.
  • Arguments:
    • params: (type = map) map containing the connection parameters
    • updateComm: (type = string) SQL command string. It may be commands: create, update, delete and drop with or without question marks.
    • columns: (type=list) list of column names of table.
    • values: (type=list) list of values that are used to replace question marks if appropriate. This is an optional parameter.
  • Exceptions: GamaRuntimeException

Examples: Using action executeUpdate do sql commands (create, insert, update, delete and drop).

map<string, string> PARAMS <- ['dbtype'::'sqlite',  'database'::'../../includes/Student.db'];
// Create table
do executeUpdate (params: PARAMS, 
                              updateComm: "CREATE TABLE registration" 
                                             + "(id INTEGER PRIMARY KEY, " 
                                             + " first TEXT NOT NULL, " + " last TEXT NOT NULL, " 
                                             + " age INTEGER);");

// Insert into 
do executeUpdate (params: PARAMS ,  
                             updateComm: "INSERT INTO registration " + "VALUES(100, 'Zara', 'Ali', 18);");
do insert (params: PARAMS, into: "registration", 
               columns: ["id", "first", "last"], 
               values: [103, 'Zaid tim', 'Kha']);

// executeUpdate with question marks
do executeUpdate (params: PARAMS,
                             updateComm: "INSERT INTO registration " + "VALUES(?, ?, ?, ?);" ,  
                             values: [101, 'Mr', 'Mme', 45]);

//update 
int n <-  executeUpdate (params: PARAMS, 
                                       updateComm: "UPDATE registration SET age = 30 WHERE id IN (100, 101)" );

// delete
int n <- executeUpdate (params: PARAMS, 
                                      updateComm: "DELETE FROM registration where id=? ",  
                                      values: [101] );

// Drop table
do executeUpdate (params: PARAMS, updateComm: "DROP TABLE registration");

MDXSKILL

MDXSKILL plays the role of an OLAP tool using select to query data from OLAP server to GAMA environment and then species can use the queried data for any analysis purposes.

Define a species that uses the MDXSKILL skill

Example of declaration:

	species olap skills: [MDXSKILL]
	 {  
		//insert your descriptions here
		
	 } 
      ...

Agents with such a skill can use additional actions (defined in the skill)

Map of connection parameters for MDX

In the actions defined in the SQLSkill, a parameter containing the connection parameters is required. It is a map with following key::value pairs:

Key Optional Description
olaptype No OLAP Server type value. Its value is a string. We must use "SSAS/XMLA" when we want to connect to an SQL Server Analysis Services by using XML for Analysis. That is the same for "MONDRIAN/XML" or "MONDRIAN" (ignore case sensitive)
dbtype No DBMS type value. Its value is a string. We must use "mysql" when we want to connect to a MySQL. That is the same for "postgres" or "sqlserver" (ignore case sensitive)
host No Host name or IP address of data server.
port No Port of connection. It is no required when we work with SQLite.
database No Name of database. It is file name include path when we work with SQLite.
catalog Yes Name of catalog. It is an optional parameter. We do not need to use it when we connect to SSAS via XMLA and its file name includes the path when we connect a ROLAP database directly by using Mondrian API (see Example as below)
user No Username.
passwd No Password.

Table 2: OLAP Connection parameter description

Example: Definitions of OLAP connection parameter

//Connect SQL Server Analysis Services via XMLA
	map<string,string> SSAS <- [
				'olaptype'::'SSAS/XMLA',
				'dbtype'::'sqlserver',
				'host'::'172.17.88.166',
				'port'::'80',
				'database'::'olap',
				'user'::'test',
				'passwd'::'abc'];

//Connect Mondriam server via XMLA
	map<string,string>  MONDRIANXMLA <- [
				'olaptype'::"MONDRIAN/XMLA",
				'dbtype'::'postgres',
				'host'::'localhost',
				'port'::'8080',
				'database'::'MondrianFoodMart',
				'catalog'::'FoodMart',
				'user'::'test',
				'passwd'::'abc'];

//Connect a ROLAP server using Mondriam API	
	map<string,string>  MONDRIAN <- [
				'olaptype'::'MONDRIAN',
				'dbtype'::'postgres',
				'host'::'localhost',
				'port'::'5433',
				'database'::'foodmart',
				'catalog'::'../includes/FoodMart.xml',
				'user'::'test',
                                'passwd'::'abc'];

Test a connection to OLAP database

Syntax:

testConnection (params: connection_parameter) The action tests the connection to a given OLAP database.

  • Return: boolean. It is:
    • true: the agent can connect to the DBMS (to the given Database with given name and password)
    • false: the agent cannot connect
  • Arguments:
    • params: (type = map) map of connection parameters
  • Exceptions: GamaRuntimeException

Example: Check a connection to MySQL

if (self testConnection(params:MONDIRANXMLA)){
	write "Connection is OK";
}else{
	write "Connection is false";
}

Select data from OLAP database

Syntax:

select (param: connection_parameter, onColumns: column_string, onRows: row_string from: cube_string, where: condition_string, values: value_list) The action creates a connection to an OLAP database and executes the select statement. If the connection or selection fails then it throws a GamaRuntimeException.

  • Return: list < list >. If the selection succeeds, it returns a list with three elements:
    • The first element is a list of column name.
    • The second element is a list of column type.
    • The third element is a data set.
  • Arguments:
    • params: (type = map) map containing the connection parameters
    • onColumns: (type = string) declare the select string on columns. The selection string can contain question marks.
    • onRows: (type = string) declare the selection string on rows. The selection string can contain question marks.
    • from: (type = string) specify cube where data is selected. The cube_string can contain question marks.
    • where_: (type = string) specify the selection conditions. The condiction_string can contains question marks. This is an optional parameter. *values: List of values that are used to replace question marks if appropriate. This is an optional parameter.
  • Exceptions:_GamaRuntimeException

Example: select data from SQL Server Analysis Service via XMLA

if (self testConnection[ params::SSAS]){
	list l1  <- list(self select (params: SSAS ,
		onColumns: " { [Measures].[Quantity], [Measures].[Price] }",
		onRows:" { { { [Time].[Year].[All].CHILDREN } * "
		+ " { [Product].[Product Category].[All].CHILDREN } * "
		+"{ [Customer].[Company Name].&[Alfreds Futterkiste], " 
		+"[Customer].[Company Name].&[Ana Trujillo Emparedadosy helados], " 
		+ "[Customer].[Company Name].&[Antonio Moreno Taquería] } } } " ,
		from : "FROM [Northwind Star] "));
	write "result1:"+ l1;
}else {
	write "Connect error";
}

Example: select data from Mondrian via XMLA with question marks in selection

if (self testConnection(params:MONDRIANXMLA)){
	list<list> l2  <- list<list> (self select(params: MONDRIANXMLA, 
	onColumns:" {[Measures].[Unit Sales], [Measures].[Store Cost], [Measures].[Store Sales]} ",
	onRows:"  Hierarchize(Union(Union(Union({([Promotion Media].[All Media],"
 	+" [Product].[All Products])}, "
	+" Crossjoin([Promotion Media].[All Media].Children, "
	+" {[Product].[All Products]})), "
	+" Crossjoin({[Promotion Media].[Daily Paper, Radio, TV]}, "
	+" [Product].[All Products].Children)), "
	+" Crossjoin({[Promotion Media].[Street Handout]}, " 
	+" [Product].[All Products].Children)))  ",
	from:" from [?] " ,
	where :" where [Time].[?] " ,
	values:["Sales",1997]));
	write "result2:"+ l2;
}else {
	write "Connect error";
}

AgentDB

AgentBD is a built-in species, which supports behaviors that look like actions in SQLSKILL but differs slightly with SQLSKILL in that it uses only one connection for several actions. It means that AgentDB makes a connection to DBMS and keeps that connection for its later operations with DBMS.

Define a species that is an inheritance of agentDB

Example of declaration:

species agentDB parent: AgentDB {  
	//insert your descriptions here
}

Connect to database

Syntax:

Connect (param: connection_parameter) This action makes a connection to DBMS. If a connection is established then it will assign the connection object into a built-in attribute of species (conn) otherwise it throws a GamaRuntimeException.

  • Return: connection
  • Arguments:
    • params: (type = map) map containing the connection parameters
  • Exceptions: GamaRuntimeException Example: Connect to PostgreSQL
// POSTGRES connection parameter
map <string, string>  POSTGRES <- [
                                        'host'::'localhost',
                                        'dbtype'::'postgres',
                                        'database'::'BPH',
                                        'port'::'5433',
                                        'user'::'postgres',
                                        'passwd'::'abc'];
ask agentDB {
      do connect (params: POSTGRES);
}

Check agent connected a database or not

Syntax:

isConnected (param: connection_parameter) This action checks if an agent is connecting to database or not.

  • Return: Boolean. If agent is connecting to a database then isConnected returns true; otherwise it returns false.
  • Arguments:
    • params: (type = map) map containing the connection parameters

Example: Using action executeUpdate do sql commands (create, insert, update, delete and drop).

ask agentDB {
	if (self isConnected){
              write "It already has a connection";
	}else{
              do connect (params: POSTGRES);
        } 
}

Close the current connection

Syntax:

close This action closes the current database connection of species. If species does not has a database connection then it throws a GamaRuntimeException.

  • Return: null If the current connection of species is close then the action return null value; otherwise it throws a GamaRuntimeException.

Example:

ask agentDB {
	if (self isConnected){
	      do close;
	}
}

Get connection parameter

Syntax:

getParameter This action returns the connection parameter of species.

  • Return: map < string, string >

Example:

ask agentDB {
	if (self isConnected){
		write "the connection parameter: " +(self getParameter);
        }
}

Set connection parameter

Syntax:

setParameter (param: connection_parameter) This action sets the new values for connection parameter and closes the current connection of species. If it can not close the current connection then it will throw GamaRuntimeException. If the species wants to make the connection to database with the new values then action connect must be called.

  • Return: null
  • Arguments:
    • params: (type = map) map containing the connection parameters
  • Exceptions: GamaRuntimeException

Example:

ask agentDB {
	if (self isConnected){
             do setParameter(params: MySQL);
             do connect(params: (self getParameter));
        }
}

Retrieve data from database by using AgentDB

Because of the connection to database of AgentDB is kept alive then AgentDB can execute several SQL queries with only one connection. Hence AgentDB can do actions such as select, insert, executeUpdate with the same parameters of those actions of SQLSKILL except params parameter is always absent.

Examples:

map<string, string> PARAMS <- ['dbtype'::'sqlite', 'database'::'../../includes/Student.db'];
ask agentDB {
   do connect (params: PARAMS);
   // Create table
   do executeUpdate (updateComm: "CREATE TABLE registration" 
	+ "(id INTEGER PRIMARY KEY, " 
        + " first TEXT NOT NULL, " + " last TEXT NOT NULL, " 
        + " age INTEGER);");
   // Insert into 
   do executeUpdate ( updateComm: "INSERT INTO registration " 
        + "VALUES(100, 'Zara', 'Ali', 18);");
   do insert (into: "registration", 
	 columns: ["id", "first", "last"], 
	 values: [103, 'Zaid tim', 'Kha']);
   // executeUpdate with question marks
   do executeUpdate (updateComm: "INSERT INTO registration VALUES(?, ?, ?, ?);",  
	 values: [101, 'Mr', 'Mme', 45]);
   //select
    list<list> t <- list<list> (self select( 
	 select:"SELECT * FROM registration;"));
    //update 
    int n <-  executeUpdate (updateComm: "UPDATE registration SET age = 30 WHERE id IN (100, 101)");
     // delete
     int n <- executeUpdate ( updateComm: "DELETE FROM registration where id=? ",  values: [101] );
     // Drop table
      do executeUpdate (updateComm: "DROP TABLE registration");
}

Using database features to define environment or create species

In Gama, we can use results of select action of SQLSKILL or AgentDB to create species or define boundary of environment in the same way we do with shape files. Further more, we can also save simulation data that are generated by simulation including geometry data to database.

Define the boundary of the environment from database

  • Step 1: specify select query by declaration a map object with keys as below:
Key Optional Description
dbtype No DBMS type value. Its value is a string. We must use "mysql" when we want to connect to a MySQL. That is the same for "postgres", "sqlite" or "sqlserver" (ignore case sensitive)
host Yes Host name or IP address of data server. It is absent when we work with SQlite.
port Yes Port of connection. It is not required when we work with SQLite.
database No Name of database. It is the file name including the path when we work with SQLite.
user Yes Username. It is not required when we work with SQLite.
passwd Yes Password. It is not required when we work with SQLite.
srid Yes srid (Spatial Reference Identifier) corresponds to a spatial reference system. This value is specified when GAMA connects to spatial database. If it is absent then GAMA uses spatial reference system defined in Preferences->External configuration.
select No Selection string

Table 3: Select boundary parameter description

Example:

map<string,string> BOUNDS <- [	
	//'srid'::'32648',
	'host'::'localhost',								
        'dbtype'::'postgres',
	'database'::'spatial_DB',
	'port'::'5433',								
        'user'::'postgres',
	'passwd'::'tmt',
	'select'::'SELECT ST_AsBinary(geom) as geom FROM bounds;' ];
  • Step 2: define boundary of environment by using the map object in first step.
geometry shape <- envelope(BOUNDS);

Note: We can do the same way if we work with MySQL, SQLite, or SQLServer and we must convert Geometry format in GIS database to binary format.

Create agents from the result of a select action

If we are familiar with how to create agents from a shapefile then it becomes very simple to create agents from select result. We can do as below:

  • Step 1: Define a species with SQLSKILL or AgentDB
species toto skills: SQLSKILL {
	//insert your descriptions here	
}
  • Step 2: Define a connection and selection parameters
global {
	map<string,string>  PARAMS <- ['dbtype'::'sqlite','database'::'../includes/bph.sqlite'];
	string location <- 'select ID_4, Name_4, ST_AsBinary(geometry) as geom from vnm_adm4 
                                      where id_2=38253 or id_2=38254;';
	...
}
  • Step 3: Create species by using selected results
init {
   create toto { 
	  create locations from: list(self select (params: PARAMS, 
		                                                   select: LOCATIONS)) 
                                   with:[ id:: "id_4", custom_name:: "name_4", shape::"geom"];
	}
   ...
}

Save Geometry data to database

If we are familiar with how to create agents from a shapefile then it becomes very simple to create agents from select result. We can do as below:

  • Step 1: Define a species with SQLSKILL or AgentDB
species toto skills: SQLSKILL {  
	//insert your descriptions here
}
  • Step 2: Define a connection and create GIS database and tables
global {
	map<string,string> PARAMS <-  ['host'::'localhost', 'dbtype'::'Postgres', 'database'::'', 
                                                            'port'::'5433', 'user'::'postgres', 'passwd'::'tmt'];

	init {
		create toto ;
		ask toto {
			if (self testConnection[ params::PARAMS]){
			    // create GIS database	
 			    do executeUpdate(params:PARAMS, 
		                updateComm: "CREATE DATABASE spatial_db with TEMPLATE = template_postgis;"); 
 			    	remove key: "database" from: PARAMS;
				put "spatial_db" key:"database" in: PARAMS;
				//create table
                            do executeUpdate params: PARAMS 
				  updateComm : "CREATE TABLE buildings "+
				  "( "  +
                   	               " name character varying(255), " + 
                                       " type character varying(255), " + 
                                       " geom GEOMETRY " + 
                                   ")";
			}else {
 				write "Connection to MySQL can not be established ";
 			}	
		}
	}
}
  • Step 3: Insert geometry data to GIS database
ask building {
   ask DB_Accessor {
	do insert(params: PARAMS, 
                        into: "buildings",
			columns: ["name", "type","geom"],
			values: [myself.name,myself.type,myself.shape];
   }
}

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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