Download AL4AI--Google2007

Using Artificial Life to
evolve Artificial
Intelligence
Virgil Griffith
California Institute of Technology
http://virgil.gr
[email protected]
Google Tech Talk - 2007
What is Artificial Life?
Life, as it is… and might have been
Origin of Life
Today
2
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 Life
to 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
Polyworld
Not to be confused with:
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
10
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





1-dimensional vision (like Flatland)
No fitness function


initial neural wiring is genetic
At birth, all neural weights are random
Hebbian learning refines synapse weights throughout lifetime
Fitness is determined by natural selection alone
Critter Colors


Red = current aggression
Blue = current horniness
QuickTime™ and a
H.264 decompressor
[Movie
- Sample]
are needed
to see this
picture.
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
Move
Turn
Eat
Mate
Fight
QuickTime™ and a
TIFF (Uncomp resse d) de com press or
are nee ded to s ee this picture.
Light
Energy Level
Focus
Random
Input Units
Processing Units
Polyworld Brain Map (actual)
18
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
QuickTime™ and a
H.264 decompressor
are needed to see this picture.
Behavior sample: Killing & Eating
QuickTime™ and a
H.264 decompressor
are needed to see this picture.
Behavior sample: Mating
QuickTime™ and a
H.264 decompressor
are needed to see this picture.
Behavior sample: Lighting
QuickTime™ and a
H.264 decompressor
are needed to see this picture.
New Species: Joggers
QuickTime™ and a
H.264 decompressor
are needed to see this picture.
New Species: Indolent Cannibals
QuickTime™ and a
H.264 decompressor
are needed to see this picture.
Emergent Behavior: Visual Response
QuickTime™ and a
H.264 decompressor
are needed to see this picture.
Emergent Behavior: Fleeing Attack
QuickTime™ and a
H.264 decompressor
are needed to see this picture.
Foraging, Grazing, Swarming
QuickTime™ and a
H.264 decompressor
are needed to see this picture.
Observations from Polyworld

Evolution generates a wide range brain
wirings

Selection for use of vision

Evolution of emergent behaviors
29
Early
Ideal Free Distribution
in agents with
evolved neural architectures
Middle
Late
Predator-Prey Cycles
31
Qu ickTim e™ a nd a
TIFF (Un comp resse d) decompresso r
are need ed to see th is picture.
Qu ickTim e™ a nd a
TIFF (Un comp resse d) decompresso r
are need ed to see th is picture.
Cat
Random
Qu ickTim e™ a nd a
TIFF (Un comp resse d) decompresso r
are need ed to see th is picture.
Polyworldian
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 selfrepresentation”
“A metabolism”
“Functional interactions with the
environment”
“The ability to evolve”
Farmer, Belin (1992)
33
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
34
Neural Functional Complexity
35
Is there an evolutionary “arrow of
complexity”?


Yes – Darwin, Lamarck, Huxley, Valentine
No – Lewontin, Levins, Gould
Carroll (2001)
Gould (1994)
Evolution drives complexity?
37
Genetic complexity over time
38
Neural Complexity: Room to grow
39
Future Directions

More…





Behavioral Ecology


Optimal foraging (profit vs. predation risk)
Evolutionary Biology



measures of complexity
complex environment
food types
agent senses (touch, smell)
Speciation = ƒ (population isolation)
Altruism = ƒ (genetic similarity)
Classical conditioning, animal intelligence experiments
40
Source Code


Source code is available!
Runs on Mac/Linux (via Qt)
http://www.sf.net/projects/polyworld/
41
But is this a good idea?
Special Thanks

Larry Yaeger

Chris Adami
43
Plasticity in Neural Function
The redirect
Mriganka Sur, et al
Science 1988, Nature 2001
Function maps
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
Distribution of orientation-selective cells in visual cortex
Monkey Cortex, Blasdel and Salama
Simulated Cortex, Ralph Linsker
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

Larry Yaeger

Chris Adami
49
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
Neural Group Mutual Information
51
Evolution drives max complexity?
52