Supplementary Software: An Information Market for Social Navigation in Robots

This repository is the Supplementary Software of a research study published in the article:

An Information Market for Social Navigation in Robots by Ludéric Van Calck, Alexandre Pacheco, Volker Strobel, Marco Dorigo, and Andreagiovanni Reina (2023).

A pre-print of the article can be found here.

This simulator explores the new idea of using a decentralized information market to regulate information exchange in a robot swarm's foraging task.

How to replicate the results from the article

In order to generate all the figures included and analysed in the article, we make available a set of configuration files this repository's configs_paper directory. The files are named as the figure; for example, by running the simulator with the configuration file configs_paper/fig2A.json, it will generate all data needed to reproduce the plot of Figure 2A in the article.

Requirements

To be able to run information-market, you must have:

• A recent version of Linux, MacOSX or Windows
• Python 3.10 or newer

Installation

First clone the git repository into a given folder on your computer:

git clone https://github.com/ludericv/information-market.git

Then, navigate into the newly created folder, create a new python virtual environment and install the required python packages:

cd information-market
python -m venv infomarket-env
source infomarket-env/bin/activate
pip install -r requirements.txt

Run

First, edit the config.json file inside the config folder with the parameters you want to use. Then, to run information-market, simply open a terminal, cd to the src folder and run the program.

cd path/to/src
python info_market.py ../config/config.json

Running Multiple Experiments

To conduct multiple experiments with different configurations, one can specify multiple configuration file paths as arguments when running the info_market.py script, interposing a space between the selected files. Example:

cd path/to/src
python info_market.py path/to/config1.json path/to/config2.json path/to/config99.json

This is mostly useful to run simulations without the GUI (set activate to false in the visualization parameters in the config files, as well as the number_runs parameter for the number of simulations you wish to perform with this configuration)

Configuration

A simulation's parameters are defined in a json configuration file (such as config/config.json). The parameters are the following:

• width: simulation environment's width
• height: simulation environment's height
• food: food area's position and radius
• nest: nest area's position and radius
• simulation_steps: simulation duration (number or time steps)
• number_runs: number of parallel simulation runs (only applicable when visualization is turned off)
• visualization: parameters related to the visualization
  • activate: whether to activate the simulation GUI
  • fps: maximum framerate of the simulation
• random_walk: random walk parameters
  • random_walk_factor: controls how much successive random walk turns are correlated
  • levi_factor: controls the duration before successive random walk turns
• agent: robot parameters (common to all robots regardless of behavior)
  • radius: robot radius
  • speed: robot speed
  • communication_radius: communication range
  • communication_stop_time: duration a robot must stop moving when communicating
  • communication_cooldown: how long a robot must wait between communications
  • noise_sampling_mu: odometric noise sampling distribution mu
  • noise_sampling_sigma: odometric noise sampling distribution sigma
  • noise_sd: odometric noise standard deviation (in degrees), i.e. how different the odometric noise at different time steps
  • fuel_cost: cost of moving at each time step (deducted from robot's monetary balance)
• behaviors: list of behaviors used in the simulation
  • class: name of the behavior class (from the code, see Behaviors section)
  • population_size: number of robots with the behavior
  • parameters: behavior-specific keyword arguments
• payment_system: payment system parameters
  • class: name of the payment system class (from the code, see Payment Systems section)
  • initial_reward: amount of money robots start the simulation with
  • parameters: payment-system-specific keyword arguments
• market: reward mechanism parameters
  • class: name of the market class (from the code, use "FixedPriceMarket", others are deprecated)
  • parameters: market-specific parameters
    • reward: reward for selling a strawberry at the nest
• data_collection: parameters for data collection
  • output_directory: output directory path
  • filename: output data filename. File will be saved to <output_directory>//filename for all metrics in metrics parameter.
  • metrics: list of metrics to record (can be "reward", "items_collected", "drifts" or "rewards_evolution").
  • precision_recording: whether to enable precision recording to save robot rewards at multiple points during the simulation. Need to add "rewards_evolution" in metrics list for the recording to be saved to a file.
  • precision_recording_interval: resolution (in number of time steps) for the precision recording

Behaviors

Robots can exhibit multiple behaviors. This sections briefly lists these behaviors and their parameters.

• NaiveBehavior: Most basic robot behavior. Simply exchanges information with everyone and uses the most recent information available.
• SaboteurBehavior: Basic dishonest robot behavior. Rotates information vectors sold to other robots by a given angle.
  • parameters:
    • rotation_angle: angle (in degrees) with which information vectors are rotated when sold.
• ScepticalBehavior: Honest robot behavior implementing basic outlier detection. If information is too different from previous belief is bought, the robot will wait until receiving information confirming the new statement or the old belief before accepting or rejecting the new information.
  • parameters:
    • threshold: controls how much new information can be different from previous belief before being considered suspicious and needing confirmation.
• ScaboteurBehavior: Saboteur behavior implementing the outlier detection from the ScepticalBehavior
  • parameters:
    • threshold: see ScepticalBehavior
    • rotation_angle: see SaboteurBehavior

Payment Systems

Payment systems implement the logic responsible for controlling the price of information.

• DelayedPaymentPaymentSystem: information is exchanged for a token that is redeemed for a fixed share of the reward the buying robot receives when it completes a round trip.
• OutlierPenalisationPaymentSystem: similar to the DelayedPaymentPayment system, but the share of the reward is proportional to how similar the information sold is to other information that was sold to the buying robot.

Visual Simulation and Hotkeys

During a simulation with GUI (activate : true in the configuration file), you can select a robot (left click on its image) and observe useful datas about it.

You can also use your keyboard to control the course of the simulation:

• SPACE: pause/resume simulation;
• N: perform one step of the simulation (useful when paused);
• TBC