1 Artificial Life: Evolving to true Artificial Intelligence Virgil Playful Technologist...

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Artificial Life:Evolving to true

Artificial Intelligence

VirgilPlayful Technologist

[email protected]

Phreaknic 9October 21, 2005

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What is Life?

Hydro-carbon chains Something that reproduces Information Processing Something that squishes when you step

on it ?

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What is Artificial Life?

Origin of Life

Today

Life, and might have beenas it is…

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What is Artificial Life?

Theoretical biology Artifactual (man-made), not fake Not explicitly designed

Bottom up, not top down Harnesses emergence and evolution

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What’s “Artificial” about A-Life? “The ‘artificial’ in Artificial Life refers to the

component parts, not the emergent processes. If the component parts are implemented correctly, the processes are genuine—every bit as genuine as the natural processes they imitate.”

“The big claim is that a proper set of artificial primitives carrying out the same functional roles as biomolecules in natural living systems will support a process that is “alive” in the same way that natural organisms are alive. Artificial Life will be genuine life—it will simply be made of different stuff than the life that has evolved here on Earth.”

— Chris Langton

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Early Artificial Life: Ross Ashby

Author of Design for a Brain (1952) and Introduction to Cybernetics (1956)

Declared internal stability to changes in the environment a defining attribute of life

Created an “ultrastable” system, the Homeostat. Device with electronic switches and four pivoting

magnets Returned to homeostasis regardless of magnitude of

perturbations.

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Homeostat

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.



[ Homeostat Java Applet ]

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Cellular Automata

First self-reproducing machine Developed by John von Neumann (1951)

Devised a 29-state 2-D cellular automata with ~85,000 cells that could make copies of itself

Design was later greatly simplified by others

Conway’s Game of Life (1970)

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Modern Artificial Life

“Agent-based” models Economics Ecological Resource Management Disease Propagation Societal Dynamics Cosmology… QuickTime™ and a

TIFF (Uncompressed) decompressorare needed to see this picture.

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Modern Artificial Life

Robotics



Rodney Brooksand COG

Cynthia Breazeland Kismet

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Evolution

Evolution is an algorithm

Given only: Variable population Selection Reproduction with occasional errors

Regardless of substrate, you get evolution!

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Evolution: An Example Problem

From random noise, lets see if evolution can find perfect squares

For convenience, lets use 0 - 100 Perfect Square’s:

[ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100 ]

Python code used for this example is available at: http://www.romanpoet.org/223

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Evolution Example: Description

Start with 25 random integers from 0 to 100. Start making copies of each number

The closer a number is a perfect square, the more copies we make

Chance of making a copy: P(x) = * (1 / <# integers from closet square>) ; =

1.5

When copying, there is a 50% chance we add/subtract 1 from the number

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Evolution Example: Selection

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Lets see what happens

Random Numbers from 0 to 100 …

After one cycle …

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And we’re done!10th Cycle

… comparing to our original random integers

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Evolution isn’t perfect

Evolution is limited by what worked before Sometimes can’t afford to lose fitness, even in

order to gain more later Once starting down a path, it can’t go back

and try again

For actual organisms Variations in core vital functions (metabolism,

etc.) can be fatal

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What can you do with Evolution?

Optimization Traffic Lights Air Foil Shape Linux Kernel Art

Fuzzy Problems Sonar response from sunken ships versus live

submarines Scientific Models of:

Computer Viruses Tribal Societies

…

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Evolution and Art - Karl Sims

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.QuickTime™ and a

TIFF (LZW) decompressorare needed to see this picture.
















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Blocky Creatures - Karl Sims Node specifies part type,

joint, and range of movement Edges specify part placement

within the entire body

Population? Graphs of Nodes and Edges

Selection? Ability to perform some

task (walking, jumping, etc.)

Reproduction/Mutation? Nodes type changed or

new nodes grafted on

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Evolved Structures

QuickTime™ and aYUV420 codec decompressor


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Artificial Life for the Evolution of Artificial

Intelligence

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What is Intelligence?

The ability to think and reason Ability to acquire and use knowledge “The ability to perceive relationships

and co-relationships between objects” SAT Score Cycles per second ?

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What is Mind?

Marionettes (ancient Greeks) Hydraulics (Descartes) Pulleys and gears (Industrial Revolution) Telephone switchboard (1930’s) Boolean logic (1940’s) Digital computer (1960’s) Neural Networks within Brains (1980’s

- ?)

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Plasticity in Function

Mriganka Sur, et alScience 1988, Nature 2001

Function maps:

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Plasticity in Wiring

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

Mriganka Sur, et al Nature 2001

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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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Neural Cooperation

John Pearson, Gerald Edelman

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Intelligence is based in brains Useful brain functions are created by:

General purpose learning mechanism Suitable initial neural architecture

Artificial neural systems capture key feature of biological neural networks

We can make a useful artificial neural systems with: Hebbian learning An evolving initial neural architecture

Neuroscience Recap

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PolyworldPolyworld

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What Polyworld Is An electronic primordial soup experiment Approaching artificial intelligence the way

natural intelligence emerged: The evolution of nervous systems in an ecology

Working our way up the intelligence spectrum

Tool for evolutionary biology, behavioral ecology, cognitive science

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What Polyworld Is Not Fully open ended

Even natural evolution is limited by physics (and previous successes)

Accurate model of microbiology Accurate model of any particular ecology

Though it could be done Accurate model of any animal’s brain

Though it could be done

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Polyworld Overview Organisms have genetic structure and evolve

over time Organisms have a physiology and metabolism Organisms have neural network “brains”

An evolving neural architecture Hebbian learning at synapses

Organisms perceive their environment through 1-dimensional vision

Fitness is determined by Natural Selection alone

Critter Colors Red = Current Aggression Blue = Current Horny’ness

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QuickTime™ and aVideo decompressor


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Genetics: Body Genes

Size Strength Maximum speed Mutation rate Lifespan Fraction of energy to offspring ID (mapped to body’s green color

component)

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Genetics: Brain Genes (Brenes)

# of neurons for red component of vision # of neurons for green component of vision # of neurons for blue component of vision

# of internal neuronal groups

# of excitatory neurons per group # of inhibitory neurons per group Initial bias of neurons per group Bias learning rate per group

Connection density per pair of groups & types Learning rate per pair of groups & types

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Neural Architecture for Critter Behavior



Random

Energy Level

Move

Turn

Eat

Mate

Fight

Light

Focus

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Physiology and Metabolism Energy is expended by behavior & neural

activity Size and strength affect behavioral energy

costs (and energy costs to opponent when attacking)

Brain size affects mental energy costs Energy is replenished by eating food (or other

organisms) Size affects maximum energy storage Health energy is distinct from carcass energy

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Behavior Sample: Eating



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Simple Behavior: Killing & Eating



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Behavior Sample: Mating



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Behavior Sample: Lighting



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Emergent Species: “Edge-runners”



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Emergent Species:“Indolent Cannibals”



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Emergent Behavior: Visual Response



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Emergent Behavior: Fleeing Attack



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Emergent Behaviors:Foraging, Grazing, Swarming



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Observations from Polyworld

Evolution of higher-order, ethological-level behaviors observed

Selection for use of vision observed Evolution of initial architectures

generates a broad range of network designs

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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.” “Internal stability under perturbations.” “The ability to evolve.”

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Information is What Matters

"Life is a pattern in space-time, rather than a specific material object.” - Farmer & Belin (1990)

Schrödinger speaks of life being characterized by and feeding on “negative entropy” (What Is Life?, 1944)

von Neumann describes brain activity in terms of information flow (The Computer and the Brain, Silliman Lectures, 1958)

Functionalism It’s the process, not the substrate What can information theory tell us about living,

intelligent processes…

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But is it Intelligent?

No obvious way to quantify intelligence (aka: We don’t know)

Lets look at a simpler case Cellular Automata and information theory

Chris Langton’s “lambda” parameter for simple cellular automata (1990) = # rules leading to nonquiescent state / #

rules (For Game of Life, = 0.273)

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CA Dynamics, = 0.0 - 0.30CA’s with low lambda die out quickly; slightly

higher lambda reaches a periodic structure in 7-10 steps

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CA Dynamics, = 0.4 - 0.45

Transients lengthen to about 60 steps,

longer periodic structures have appeared

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CA Dynamics, = 0.50Typical transient on order of 12,000 steps. Dynamics may settle into periodic structure, but tendency is to expand.

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CA Dynamics, = 0.55 - 0.60

Transients begin to shorten; dynamics become

chaotic; then random

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Quantifying Complexity

I

II

IV

III

Wolfram's CA classes:

I = Fixed II = PeriodicIII = Chaotic IV = Complex

0.0 1.0Low

High

Complexity

c=0.5Lambda

Is there a “lambda” for neural systems?

Some early work, but not yet We’re working on it

Complexity = length of transients

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Quantifying Intelligence of Polyworldians Measure state and compute

complexity

Which state? Aspects of behavior Neural structure Neural activity

Which complexity? Shannon Mutual Information (1963) Tononi’s functional complexity (1994) Gell-Mann & Lloyd’s “effective” complexity

(1995)

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Cat



Polyworldian



Random

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Polyworld: Future Directions Record measure(s) of complexity More complex environment

Multiple food types Additional senses (touch, smell, etc.) Behavioral Ecology

Optimal foraging (profit vs. predation risk) Niche Formation (Vancouver whales)

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 (on Qt)

Technically should work on Windows Version 1.0 Released Oct 20, 2005!

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

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Credits

Larry Yaeger Doyne Farmer Wikipedia

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