Using Artificial Life to evolve Artificial Intelligence Virgil Griffith California Institute of Technology [email protected] Google Tech

Using Artificial Life to evolve Artificial

Intelligence

Virgil GriffithCalifornia Institute of Technology

http://[email protected]

Google Tech Talk - 2007

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What is Artificial Life?

Origin of Life

Today

Life, and might have beenas it is…

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

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

[Blocky Creatures Movie]

Using Artificial Lifeto evolve

Artificial 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

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

PolyworldPolyworld

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Not to be confused with:

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

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 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 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

[Movie - Sample]

QuickTime™ and aH.264 decompressor


Body Genes Size Strength Max speed Max lifespan Fraction of energy given to offspring Greenness Point-mutation rate Number of crossover points

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



Random

Energy Level

Move

Turn

Eat

Mate

Fight

Light

Focus

Input Units Processing Units

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Polyworld Brain Map (actual)

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

Behavior sample: Eating



Behavior sample: Killing & Eating



Behavior sample: Mating



Behavior sample: Lighting



New Species: Joggers



New Species: Indolent Cannibals



Emergent Behavior: Visual Response



Emergent Behavior: Fleeing Attack



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

Ideal Free Distributionin agents with

evolved neural architectures

Early

Middle

Late

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Predator-Prey Cycles



Cat



Polyworldian



Random

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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

Is there an evolutionary “arrow of complexity”? Yes – Darwin, Lamarck, Huxley, Valentine No – Lewontin, Levins, Gould

Gould (1994)Carroll (2001)

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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)

http://www.sf.net/projects/polyworld/

But is this a good idea?

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Special Thanks

Larry Yaeger Chris Adami

Plasticity in Neural Function

Mriganka Sur, et alScience 1988, Nature 2001

Function mapsThe redirect

Plasticity in Wiring

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

Mriganka Sur, et al. Nature 2001

Hebbian Learning: Structure from Randomness

John Pearson, Gerald Edelman

Real and Artificial Brain Maps

Monkey Cortex, Blasdel and Salama Simulated Cortex, Ralph Linsker

Distribution of orientation-selective cells in visual cortex

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

Neuroscience Recap

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Thanks to

Larry Yaeger Chris Adami

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?

Documents

Using Artificial Life to evolve Artificial Intelligence Virgil Griffith California Institute of Technology [email protected] Google Tech