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README.md
agents.py
colony.py
environment.py
inerts.py
map.py
seed.py
shared.py
system.py

README.md

unts was a term project for an Emergent Computing class taught by Dr. Christian Jacob in 2008. It was built using Python 2.3 and Breve 2.7.2. It is extensively documented and should be a suitable starting point for other students looking for project platforms or inspiration.

unts is a relatively massive, highly flexible emergence framework and technical demonstration that simulates multiple ant-like colonies that search for food and water, reproduce, establish new hills, fight off menacing spider-like enemies, and battle amongst themselves for control of resources, all based on local environmental information without any sort of centralized AI or explicit communication mechanics.


Overview

This project's outline and implementation plan are summarized in its proposal document. It's a lengthy read, but most of the math can be ignored unless you plan to do more than simply change configuration values to see what will happen.

A post-mortem write-up is also provided and should be consulted prior to using this system as a basis for other projects.


Project information

Videos

  • A short resource-gathering simulation (1.2MB)
    • Notice how the workers gradually determine an optimum path to the resources, with disruptions as resources are depleted
  • An example showing workers fleeing an attack (2.6MB)
    • Observe how the workers are unable to form efficient paths and are effectively cornered by the warriors
  • A demonstration of warriors defending workers from threats (3.2MB)
    • When the next reproduction cycle hits, the colony, knowing that it's vulnerable, emphasizes warrior production
  • What happens when no warriors are around to save workers (3.7MB)
    • Finding a great environment, the threats thrive and reproduce, decimating the colony's chances for survival
  • A large simulation used to demonstrate the project (26.7MB)
    • Unfortunately, this render was made against a buggy version (there wasn't time to fix it), so workers will actually assign a very low (reciprocal) score to resources within their core sense-of-smell radius. This results in the best-established colonies -- those that found new hills and seem to be thriving -- starving because they estimate that resource acquisition will occur faster than it does.
      • Despite this bug, there's a clear demonstration of emergent expansion, path-finding, defense (as warriors seek out threats based on lingering pheromones), avoidance, and a whole ton of math, demonstrating what can be done with a very simple configuration file and showing how small changes to the parameters of two colonies can produce dramatically different results, even with identical environments.
    • It's really pretty to watch.

Development materials

Getting help

If you're stuck or curious about how something works, don't hesitate to ask. You should get a response fairly quickly.


Feedback

If you found unts helpful in any way, let us know. If you really need a specific extension to make it work for you as part of a project, just ask. Emergence is a great field and we'd love to explore it with you.


Credits

Neil Tallim

  • Programming, design

Contacts

flan {at} uguu {dot} ca