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Super Ants!!

Super Ants!!. Matt deWet & David Robson. Symbiotic Coevolution. Primary research question: “Can heterogeneous teams of evolving agents, who depend upon each other for survival, learn to work together?”. How to test that. Environment needed: Two specialized teams of agents, run by NEAT

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Super Ants!!

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  1. Super Ants!! Matt deWet & David Robson

  2. Symbiotic Coevolution • Primary research question: • “Can heterogeneous teams of evolving agents, who depend upon each other for survival, learn to work together?”

  3. How to test that • Environment needed: • Two specialized teams of agents, run by NEAT • Different abilities, different roles • Can only survive by working together

  4. Our Environment • Ants! • Soldiers & Workers • Environmental Threats • Spiders • These love the taste of worker flesh • Controlled by a static algorithm • Starvation • Great at killing spiders • Not so great at gathering food

  5. Our Environment (cont’d) • The world • Bounded grid of variable size • Randomly placed food • Randomly spawned enemies • Movement • All entities move at most one space at a time on the grid • Movements all take place simultaneously, so no unit has an advantage

  6. The Plan • Sensors • Soldiers can see nearby enemies and workers • Workers can see nearby food, enemies, and soldiers • Desired behavior • Soldiers learn to keep foraging workers safe • How can we tell? • Overall fitness? • Inspection

  7. The Experiment • Control • Evolve the two groups separately, then stick them together and see how they do • Experiment • Evolve the two populations together, observe behavior • Variations: • Pre-evolved or un-evolved brains.

  8. Current Work

  9. Current Work • Current fitness functions • Soldiers • fitness: Spiders killed • Workers • fitness: How much food is eaten • Some videos!

  10. Multi-tiered Networks • Neural network acts as a switch between behaviors • Behaviors implemented as neural networks or algorithms • Simplifies each network • Minimizes inputs • Splits large tasks into learnable chunks

  11. Multi-tiered Networks (cont’d) • Advantages • Intuitive • Smaller and less complex networks • Generally faster than traditional AI algorithms • Disadvantages • More human labor-intensive for development/design • Some tasks may not be easily divisible

  12. Future work • Shared fitness • Reward for colony doing well • More important for soldiers • Problem: • Any shared fitness among all agents in one population is nullified, because only relative fitness is used to determine who reproduces. 

  13. Future work • Alternate fitness functions • Slightly more engineered • Updated sensors • Add nearby ants • Blob sensors • Various engine additions • Set up environment by hand • Run multiple experiments in parallel (in progress) • … Starvation

  14. Questions? Funny ant stories?

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