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Using Artificial Life to evolve Artificial Intelligence. Virgil Griffith California Institute of Technology Google Tech Talk - 2007. as it is…. and might have been. Origin of Life. Today. What is Artificial Life?. Life,. Evolution: an abbrev intro.

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using artificial life to evolve artificial intelligence

Using Artificial Life to evolve Artificial Intelligence

Virgil Griffith

California Institute of Technology

Google Tech Talk - 2007

what is artificial life

as it is…

and might have been

Origin of Life


What is Artificial Life?


evolution an abbrev intro
Evolution: an abbrev intro
  • Evolution is an algorithm
  • Given only:
    • Variable population
    • Selection
    • Reproduction with occasional errors

Regardless of substrate, you get evolution!

forming body plans with evolution
Forming body plans with evolution
  • Node specifies part type, joint, and range of movement
  • Edges specify the joints between parts
  • Population?
    • Graphs of nodes and edges
  • Selection?
    • Ability to perform some task (walking, jumping, etc.)
  • Mutation?
    • Node types change/new nodes grafted on
how to model intelligence
How to model Intelligence?
  • Marionettes (ancient Greeks)
  • Hydraulics (Descartes)
  • Pulleys and gears (Industrial Revolution)
  • Telephone switchboard (1930’s)
  • Boolean logic (1940’s)
  • Digital computer (1960’s)
  • Neural networks (1980’s - ?)
nervous systems
Nervous Systems
  • Evolution found and stuck with nervous systems across all levels of complexity
    • Provide all behaviors—including anything that might be considered intelligence—in all organisms more complex than plants
    • Some behaviors are innate, so the wiring diagram (the connections) must matter
    • But some behaviors are learned, so learning—phenotypic plasticity—must also matter
what polyworld is
What Polyworld is
  • Making artificial intelligence the way Nature made natural intelligence:
    • The evolution of nervous systems in an ecology
  • Working our way up the intelligence spectrum
  • Research tool for evolutionary biology, behavioral ecology, cognitive science
what polyworld is not
What Polyworld is not
  • Fully open ended
  • Accurate model of microbiology
  • Accurate model of any particular ecology
    • though could be done
  • Accurate model of any animal’s brain
    • though could be done
polyworld overview
Polyworld Overview
  • Organisms have:
    • evolving genes, and mate sexually
    • a body and metabolism
    • neural network brains
      • initial neural wiring is genetic
      • At birth, all neural weights are random
      • Hebbian learning refines synapse weights throughout lifetime
    • 1-dimensional vision (like Flatland)
  • No fitness function
    • Fitness is determined by natural selection alone
  • Critter Colors
    • Red = current aggression
    • Blue = current horniness
body genes
Body Genes
  • Size
  • Strength
  • Max speed
  • Max lifespan
  • Fraction of energy given to offspring
  • Greenness
  • Point-mutation rate
  • Number of crossover points
brain genes
Brain Genes
  • Vision
    • # of neurons for seeing red
    • # of neurons for seeing green
    • # of neurons for seeing blue
  • # of internal neural groups
  • For each neural group…
    • # of excitatory neurons
    • # of inhibitory neurons
    • Initial bias of neurons
    • Bias learning rate
  • For each pair of neural groups…
    • Connection density for excitatory neurons
    • Connection density for inhibitory neurons
    • Learning rate for excitatory neurons
    • Learning rate for inhibitory neurons
polyworldian brain map








Energy Level


Input Units

Processing Units

Polyworldian brain map
all about energy health
All about Energy (Health)
  • Get Energy by:
    • eating food pellets
    • eating other Polyworldians
  • Lose Energy by:
    • mating, moving, existing
    • having large size or strength
      • but get benefits in max-energy and fighting
    • brain activity
      • for computational reasons and parsimonious brain size
observations from polyworld
Observations from Polyworld
  • Evolution generates a wide range brain wirings
  • Selection for use of vision
  • Evolution of emergent behaviors

Ideal Free Distribution

in agents with

evolved neural architectures








but is it alive ask farmer belin
But is it Alive? Ask Farmer & Belin…
  • “Life is a pattern in space-time, rather than a specific material object”
  • “Self-reproduction”
  • “Information storage of a self-representation”
  • “A metabolism”
  • “Functional interactions with the environment”
  • “The ability to evolve”

Farmer, Belin (1992)

but is it intelligent
But is it Intelligent?
  • No obvious way to measure intelligence
    • (aka: We don’t know)
    • even biologists have a hard time on this
  • But we’re in a simulation, that means we can use techniques not available to biology!
    • Information theory
    • Complexity theory

Gould (1994)

Carroll (2001)

Is there an evolutionary “arrow of complexity”?

  • Yes – Darwin, Lamarck, Huxley, Valentine
  • No – Lewontin, Levins, Gould
future directions
Future Directions
  • More…
    • measures of complexity
    • complex environment
    • food types
    • agent senses (touch, smell)
  • Behavioral Ecology
    • Optimal foraging (profit vs. predation risk)
  • Evolutionary Biology
    • Speciation = ƒ (population isolation)
    • Altruism = ƒ (genetic similarity)
  • Classical conditioning, animal intelligence experiments
source code
Source Code
  • Source code is available!
  • Runs on Mac/Linux (via Qt)

special thanks
Special Thanks
  • Larry Yaeger
  • Chris Adami
plasticity in neural function
Plasticity in Neural Function

Function maps

The redirect

Mriganka Sur, et al

Science 1988, Nature 2001

plasticity in wiring
Plasticity in Wiring

Patterns of long-range connections in V1, normal A1, and rewired A1

Mriganka Sur, et al. Nature 2001

real and artificial brain maps

Monkey Cortex, Blasdel and Salama

Simulated Cortex, Ralph Linsker

Real and Artificial Brain Maps

Distribution of orientation-selective cells in visual cortex

neuroscience recap
Neuroscience Recap
  • Intelligence is based in brains
  • Useful brain functions are created by a:
    • suitable initial neural wiring
    • general purpose learning mechanism
  • Artificial neural networks capture key features of biological neural networks
  • Thus, we could make useful artificial neural systems with:
    • An evolving population of wiring diagrams
    • Hebbian learning
thanks to
Thanks to
  • Larry Yaeger
  • Chris Adami
what can evolution do
What can Evolution do?
  • Optimization
    • Traffic Lights
    • Air Foil Shape
  • Fuzzy Problems
    • Sonar response from sunken ships versus live submarines
    • Good for management tasks, such as timetables and resource scheduling
    • Even good for evolving learning algorithms and simulated organisms and behaviors