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BDIAgents_step4

Baptiste Lesquoy edited this page May 9, 2022 · 12 revisions

4. Emotions and Personality

This fourth step consists of adding emotions that will impact the gold miner agent behavior and defining the personality of the agents.

Formulation

  • Definition of global emotions
  • Modification of the miner species to integrate emotions and personality

Emotions

The BDI architecture of GAMA gives the possibility to generate emotions and to use them in the cognition. The definition of emotions in GAMA is based on the OCC theory of emotions. According to this theory, an emotion is a valued answer to the appraisal of a situation. In GAMA an emotion is represented by a set of 5 elements:

  • E: the name of the emotion felt by agent i.
  • P: the predicate that represents the fact about which the emotion is expressed.
  • A: the agent causing the emotion.
  • I: the intensity of the emotion.
  • D: the decay of the emotion's intensity.

The BDI architecture of GAMA integrates a dynamic creation of emotions process that will create emotions according to the mental states of the agent. More precisely, twenty emotions can be created: eight emotions related to events, four emotions related to other agents and eight emotions related to actions.

The complete description of these emotions and their creation rules can be found in (Bourgais et al., 2017).

Personality

In order to facilitate the parametrization of the BDI agents, we add the possibility to define all the parameters related to the BDI architecture through the OCEAN model, which proposes to represent the personality of a person according to five factors (corresponding to the 5 variables of the BDI agents):

  • O: represents the openness of someone (open-minded/narrow-minded).
  • C: represents the consciousness of someone (act with preparations/impulsive).
  • E: represents the extroversion of someone (extrovert/shy).
  • A: represents the agreeableness of someone (friendly/hostile).
  • N: represent the degree of control someone has on its emotions (calm/neurotic)

Each of these variables has a value between 0 and 1. 0.5 represents the neutral value, below 0.5, the value is considered negatively and above 0.5, it is considered positively. For example, someone with a value of 1 for N is considered as calm and someone with a value of 0 for A is considered as hostile.

Model Definition

Emotions

We add a new global emotion called joy that represents the joy emotion.

global {
    ...
    emotion joy <- new_emotion("joy");
    ...
}

Emotions and personality

To use emotions (and to activate the automatic emotion generation process), we just have to set the value of the built-in variable use_emotions_architecture to true (false by default). In our case, one of the possible desires concerns the predicate has_gold, and when an agent fulfill this desire and find a gold nugget (plan get_gold), it gets the belief has_gold, and the emotion engine automatically creates a joy emotion.

To be able to define the parameter of a BDI agent through the OCEAN model, we have to set the value of the built-in variable use_personality to true (false by default). In this model, we chose to use the default value of the O, C, E, A and N variables (default value: 0.5). The interest of using the personality in our case is to allow the emotion engine to give a lifetime to the created emotions (otherwise, the emotions would have an infinite lifetime).

In this model, we only use the emotions to define if the miner agents are going to share or not its knowledge about the gold mines. We consider that the miner only shares information if it has a joy emotion (and the agent tells that it is joyfous).

species miner skills: [moving] control: simple_bdi {
    ...
    bool use_emotions_architecture <- true;
    bool use_personality <- true;
		
    perceive target: gold_mine where (each.quantity > 0) in: view_dist {
	focus mine_at_location var:location;
	ask myself {
	    if (has_emotion(joy)) { 
                write self.name + " is joyous";
                do add_desire(predicate:share_information, strength: 5.0);
            }
	    do remove_intention(find_gold, false);
	}
    }
    ...
}

Complete Model

https://github.com/gama-platform/gama/blob/GAMA_1.8.2/msi.gaml.architecture.simplebdi/models/BDI%20Architecture/models/Tutorial/BDI%20tutorial%204.gaml

Back to the start of the tutorial

  1. Creation of the basic model: gold mines and market
  2. Definition of the BDI miners
  3. Definition of social relations between miners
  4. Adding norms, obligations and enforcement
  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
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