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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

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Using artificial life to evolve artificial intelligence l.jpg

Using Artificial Life to evolve Artificial Intelligence

Virgil Griffith

California Institute of Technology

Google Tech Talk - 2007

What is artificial life l.jpg

as it is…

and might have been

Origin of Life


What is Artificial Life?


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Evolution: an abbrev intro

  • Evolution is an algorithm

  • Given only:

    • Variable population

    • Selection

    • Reproduction with occasional errors

      Regardless of substrate, you get evolution!

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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

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[Blocky Creatures Movie]

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Using Artificial Lifeto evolveArtificial Intelligence

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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 - ?)

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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

Not to be confused with l.jpg

Not to be confused with:

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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

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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

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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

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[Movie - Sample]

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Body Genes

  • Size

  • Strength

  • Max speed

  • Max lifespan

  • Fraction of energy given to offspring

  • Greenness

  • Point-mutation rate

  • Number of crossover points

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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

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Energy Level


Input Units

Processing Units

Polyworldian brain map

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Polyworld Brain Map (actual)

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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

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Behavior sample: Eating

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Behavior sample: Killing & Eating

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Behavior sample: Mating

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Behavior sample: Lighting

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New Species: Joggers

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New Species: Indolent Cannibals

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Emergent Behavior: Visual Response

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Emergent Behavior: Fleeing Attack

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Foraging, Grazing, Swarming

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Observations from Polyworld

  • Evolution generates a wide range brain wirings

  • Selection for use of vision

  • Evolution of emergent behaviors

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Ideal Free Distribution

in agents with

evolved neural architectures




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Predator-Prey Cycles

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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)

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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

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Neural Functional Complexity

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Gould (1994)

Carroll (2001)

Is there an evolutionary “arrow of complexity”?

  • Yes – Darwin, Lamarck, Huxley, Valentine

  • No – Lewontin, Levins, Gould

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Evolution drives complexity?

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Genetic complexity over time

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Neural Complexity: Room to grow

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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

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Source Code

  • Source code is available!

  • Runs on Mac/Linux (via Qt)

But is this a good idea l.jpg

But is this a good idea?

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Special Thanks

  • Larry Yaeger

  • Chris Adami

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Plasticity in Neural Function

Function maps

The redirect

Mriganka Sur, et al

Science 1988, Nature 2001

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Plasticity in Wiring

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

Mriganka Sur, et al. Nature 2001

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Hebbian Learning: Structure from Randomness

John Pearson, Gerald Edelman

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Monkey Cortex, Blasdel and Salama

Simulated Cortex, Ralph Linsker

Real and Artificial Brain Maps

Distribution of orientation-selective cells in visual cortex

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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

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Thanks to

  • Larry Yaeger

  • Chris Adami

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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

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Neural Group Mutual Information

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Evolution drives max complexity?

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