super ants!!
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Super Ants!!Matt deWet & David Robson
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Symbiotic CoevolutionPrimary research question:
“Can heterogeneous teams of evolving agents, who depend upon each other for survival, learn to work together?”
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How to test thatEnvironment
needed:Two specialized
teams of agents, run by NEAT Different abilities,
different roles Can only survive by
working together
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Our EnvironmentAnts!
Soldiers & WorkersEnvironmental Threats
Spiders These love the taste of
worker flesh Controlled by a static
algorithmStarvation
Great at killing spiders Not so great at gathering
food
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Our Environment (cont’d)The world
Bounded grid of variable sizeRandomly placed foodRandomly spawned enemies
MovementAll entities move at most one space at a time
on the gridMovements all take place simultaneously, so no
unit has an advantage
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The PlanSensors
Soldiers can see nearby enemies and workersWorkers can see nearby food, enemies, and
soldiersDesired behavior
Soldiers learn to keep foraging workers safeHow can we tell?
Overall fitness? Inspection
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The ExperimentControl
Evolve the two groups separately, then stick them together and see how they do
ExperimentEvolve the two
populations together, observe behavior
Variations: Pre-evolved or un-
evolved brains.
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Current Work
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Current WorkCurrent fitness
functionsSoldiers
fitness: Spiders killed
Workers fitness: How
much food is eaten
Some videos!
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Multi-tiered NetworksNeural network acts as
a switch between behaviorsBehaviors implemented
as neural networks or algorithms
Simplifies each networkMinimizes inputsSplits large tasks into
learnable chunks
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Multi-tiered Networks (cont’d)Advantages
IntuitiveSmaller and less complex networksGenerally faster than traditional AI algorithms
DisadvantagesMore human labor-intensive for development/designSome tasks may not be easily divisible
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Future workShared 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.
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Future workAlternate fitness functions
Slightly more engineeredUpdated sensors
Add nearby antsBlob sensors
Various engine additionsSet up environment by handRun multiple experiments in parallel (in
progress)… Starvation
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Questions?Funny ant stories?