/Research_CPS_SupplyChain-MultiAgent

Primary LanguageShell

Project About Action Theory for Supply Chain and other Misceleneous components

Project Structure

A Node.JS API backend + frontend for the hybrid ASP + Ontology Reasoner.

It consists of the following folders:

  1. client_src
  2. server_src
  3. src

An agent environment coordination program for testing action theory with Clingo.

It consists of the following folders:

  1. agent_environment:

    a. src: the source folder of the base codes.
    b. scenarios: containing Clingo action theory files for experimenting. c. config files (files ending with "..._config.json"): defining the files needed by each agent's and environment.

The src folder contains the following files:

  1. agent.py: the code for running individual agent.
  2. env.py: the code for running the environment.
  3. env_misc.py: some functions that env.py needs.
  4. misc.py: some codes that both the environment and agent needs.
  5. parser_factory.py: A parser factory to generate parsers to get fluent and actions' names.
  6. planner.py: A planner defining the behaviour of each individual agent.

Prerequisites

  1. Install mosquitto following the instruction at https://mosquitto.org/download/:

    Mosquitto is a MQTT broker (pubish-subscriber model) used to handle communication between agent and environemnt.

  2. Install paho-mqtt module for agent and environment to talk to the broker:

     pip install paho-mqtt

    Link: https://pypi.org/project/paho-mqtt/

How to run

  1. Start the MQTT broker (if not running yet, the instructions will be displayed at the time of install). You can run:

    MacOS:

     brew services start mosquitto

    or MacOs:

     mosquitto -c /usr/local/etc/mosquitto/mosquitto.conf

  2. Start the environment:

    a. In a different terminal tab/session, go to the src folder of agent_environment.

    b. Run:

     python3 env.py <path_to_env_config>

    For example:

     python3 env.py ../env_config.json

  3. Start the agent

    a. In a different terminal tab/session, go to the src folder of agent_environment.

    b. Run:

     python3 agent.py <path_to_agent_config>

    For example:

     python3 agent.py ../agent1_config.json

    and

     python3 agent.py ../agent2_config.json

Run OEC

  1. Run environment.

    make env

  2. Run car producer

    make ID=car_producer agent

  3. Run speedy auto part

    make ID=speedy_auto_part agent

  4. Run supplier of speedy auto part.

    make ID=supplier_of_speedy_auto_part agent