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Super Ants!! Matt deWet & David Robson

Super Ants!!

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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 Presentation

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Page 1: Super Ants!!

Super Ants!!Matt deWet & David Robson

Page 2: Super Ants!!

Symbiotic CoevolutionPrimary research question:

“Can heterogeneous teams of evolving agents, who depend upon each other for survival, learn to work together?”

Page 3: Super Ants!!

How to test thatEnvironment

needed:Two specialized

teams of agents, run by NEAT Different abilities,

different roles Can only survive by

working together

Page 4: Super Ants!!

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

Page 5: Super Ants!!

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

Page 6: Super Ants!!

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

Page 7: Super Ants!!

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.

Page 8: Super Ants!!

Current Work

Page 9: Super Ants!!

Current WorkCurrent fitness

functionsSoldiers

fitness: Spiders killed

Workers fitness: How

much food is eaten

Some videos!

Page 10: Super Ants!!

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

Page 11: Super Ants!!

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

Page 12: Super Ants!!

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.

Page 13: Super Ants!!

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

Page 14: Super Ants!!

Questions?Funny ant stories?