using artificial life to evolve artificial intelligence virgil griffith california institute of...
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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…
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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 evolve
Artificial 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
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PolyworldPolyworld
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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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Polyworldian brain map
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Random
Energy Level
Move
Turn
Eat
Mate
Fight
Light
Focus
Input Units Processing Units
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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 Distributionin agents with
evolved neural architectures
Early
Middle
Late
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Predator-Prey Cycles
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Cat
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Polyworldian
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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
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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/
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But is this a good idea?
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Special Thanks
Larry Yaeger Chris Adami
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Plasticity in Neural Function
Mriganka Sur, et alScience 1988, Nature 2001
Function mapsThe redirect
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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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Real and Artificial Brain Maps
Monkey Cortex, Blasdel and Salama Simulated Cortex, Ralph Linsker
Distribution of orientation-selective cells in visual cortex
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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
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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?