Skip to content

BatchSpecific

Tri Nguyen-Huu edited this page May 6, 2022 · 19 revisions

Batch Specific UI

When an experiment of type Batch is run, a dedicated UI is displayed, depending on the parameters to explore and of the exploration methods.

Table of contents

Information bar

In batch mode, the top information bar displays 3 distinct information (instead of only the cycle number in the GUI experiment):

  • The run number: One run corresponds to N executions of simulation with one given parameters values (N is an integer given by the facet repeat in the definition of a batch experiment. The number of runs is chosen by the exploration method).
  • The simulation number: the number of replications done (and the number of replications specified with the repeat facet);
  • The number of thread: the number of threads used for the simulation.

images/batch_Information_bar.png

Batch UI

The parameters view is also a bit different in the case of a Batch UI:

  • it shows both the parameters of the experiment, with a distinction between the ones that will be explored and the ones that will not.
  • it also shows the state of the exploration. The provided information will depend on the exploration method.

The following interface is generated given the following experiment (the exploration method is here the exhaustive one):

experiment Batch type: batch repeat: 2 keep_seed: true until: (food_gathered = food_placed) or (time > 400) {
   parameter 'Size of the grid:' var: gridsize init: 75 unit: 'width and height';
   parameter 'Number:' var: ants_number init: 200 unit: 'ants';
   parameter 'Evaporation:' var: evaporation_per_cycle among: [0.1, 0.2, 0.5, 0.8, 1.0] unit: 'rate every cycle (1.0 means 100%)';
   parameter 'Diffusion:' var: diffusion_rate min: 0.1 max: 1.0 unit: 'rate every cycle (1.0 means 100%)' step: 0.3;

   method exhaustive maximize: food_gathered;
}

The batch UI for an exhaustive exploration method.

The interface summarises all model parameters and the parameters given to the exploration method:

  • Environment and Population: displays all the model parameters that should not be explored.
  • Parameters to explore: the parameters to explore are the parameters defined in the experiment with a range of values (with among facet or min, max and step facets);
  • Exploration method: it summarises the Exploration method and the stop condition. For the exhaustive method, it also computes the parameter space. For other methods, it also displays the method parameters (e.g. mutation or crossover probability...). Finally, the best and the last fitnesses found are displayed (with the associated parameter set).

The following interface with the same experiment as previously, with only one difference: the exploration method is the genetic method.

experiment Batch type: batch repeat: 2 keep_seed: true until: (food_gathered = food_placed) or (time > 400) {
   // [Parameters]
   method genetic maximize: food_gathered;
}

The batch UI for a genetic exploration method.

  1. What's new (Changelog)
  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
  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
  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
  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
  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

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

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

Resources

  1. Videos
  2. Conferences
  3. Code Examples
  4. Pedagogical materials
Clone this wiki locally