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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

Text of Super Ants!!

Super Ants

Super Ants!!Matt deWet & David RobsonSymbiotic CoevolutionPrimary research question:Can heterogeneous teams of evolving agents, who depend upon each other for survival, learn to work together?How to test thatEnvironment needed:Two specialized teams of agents, run by NEATDifferent abilities, different rolesCan only survive by working together

Our EnvironmentAnts!Soldiers & WorkersEnvironmental ThreatsSpidersThese love the taste of worker fleshControlled by a static algorithmStarvationGreat at killing spidersNot so great at gathering food

Our Environment (contd)The worldBounded grid of variable sizeRandomly placed foodRandomly spawned enemiesMovementAll entities move at most one space at a time on the gridMovements all take place simultaneously, so no unit has an advantageThe PlanSensorsSoldiers can see nearby enemies and workersWorkers can see nearby food, enemies, and soldiersDesired behaviorSoldiers learn to keep foraging workers safeHow can we tell?Overall fitness?InspectionThe ExperimentControl Evolve the two groups separately, then stick them together and see how they doExperimentEvolve the two populations together, observe behaviorVariations:Pre-evolved or un-evolved brains.

Current Work

Current WorkCurrent fitness functionsSoldiersfitness: Spiders killedWorkersfitness: How much food is eaten

Some videos!

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

Multi-tiered Networks (contd)AdvantagesIntuitiveSmaller and less complex networksGenerally faster than traditional AI algorithmsDisadvantagesMore human labor-intensive for development/designSome tasks may not be easily divisibleFuture workShared fitnessReward for colony doing wellMore important for soldiersProblem:Any shared fitness among all agents in one population is nullified, because only relative fitness is used to determine who reproduces.

Future workAlternate fitness functionsSlightly more engineeredUpdated sensorsAdd nearby antsBlob sensorsVarious engine additionsSet up environment by handRun multiple experiments in parallel (in progress) StarvationQuestions?Funny ant stories?