Ant Colony Evolution
Created by W. Craig Jones, Sam Shresha, Mahdi Judeh, Sam Fadrigalan

Goal:
    Have a colony of ants efficiently harvest food.

WBS: 
    * Get Client to generate a random simulation if given a specific flag
        * Load some settings from the local Redis
        * Generate a random environment
        * Generate a random genetic string for the FF NN
            * Make this a class in shared that implements a standard interface.
        * Run the simulation with lots of logging.
        * Tweak settings and optimize until a simulation takes under 1s (This may be hard, but is important due to search space size.)
            * If the environment is 256x256, try to have the simulation last at least 65536 steps.

!!! CONGRATS VERSION 0.5

    * Get the Server to read the config file and push settings to Redis
    * Get the Client to connect to Redis and load settings
    * Get the Server to generate random a random simulation with the redis structure correct.
        * If PG is simpler than expected, add that in now but don't waste time on it.
    * Get the Client to pull a simulation from Redis, run it, and push the results
    * Get the Server talking to PG if it isn't
    * Add a flag to the server to just generate many random simulations
        * This experiment will just be finding a starting point for the genetic algorithm, and testing performance
        * Server clears the result list to the database, then fills the simulation list back up to 100 entries.
        * Should run each entity against the same 10 environment seeds.
    * Get the docker cluster working
        * Add a flag to the clients so they stay alive and just keep trying to pull simulations.
    * Start the server to search for the initial population
        * Mark these in the database as an initial search of the particular AI type and version
        * Periodically delete the data blobs from everything but the best 1000
!!! This is the point to simplify the simulation if we aren't finding any decent ants
    * The search space is HUGE, so give it at least 1M tries
        * 1M tries should be under 1GB and take under 8 hours
    * Start all of the docker clients to search for the docker population.
        * Possibly get a web graphing tool going if someone else in the group can do it.
        * NodeJS endpoints using restify for simplicity.
    * Add a configuration to the server to start a new experiment with a population from the initial search
        * Get the genetic algorithm working kinda sorta
    * Start this and see if it works in a reasonable amount of time.
        * Reasonable being 12 hours indicating progress.

!!! CONGRATS VERSION 1.0

    * Add a LCS AI
    * Add a NN with some kind of Memory

!!! CONGRATS VERSION 2.0

    * Start iterating on the AIs
    * Start iterating on the graphics.

Implementation:
    Several Computational Intelligence Strategies
    Genetic Algorithm powering the CIs
    Distributed Computing powering the GA

Experimentation:
    How do different AIs compare?
    How do different GAs compare?

Requirements:
    * Docker is installed and working, with the daemon docker-machine running.
        https://docs.docker.com/engine/getstarted/step_one/#step-1-get-docker
    * You have sufficient space for docker images. (>10GB (total guess))
    * You have a PostgreSQL 9.6.1 database running:
        * With the acs44 database added
        * With settings correctly added to:
            ./shared/config.h

Compliation:
    ./scripts/build_server
    ./scripts/build_client
    Maybe: ./scripts/build_test

    * Docker does everything magically for you. Well not magically, just follow the scripts if you want enlightenment.