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=x agent
   

3. Run speedy auto part:

    make ID=a agent
   

4. Run supplier of speedy auto part:

    make ID=as agent
   

5. Run precision engine:

    make ID=p agent
   

6. Run metal crafts:

    make ID=m agent
   

7. Run supplier of metal crafts:

   make ID=ms agent
   

8. Run sonic electronics:

   make ID=e agent
   

9. Run supplier of sonic electronics:

   make ID=es agent