Artificial%20life%20and%20artificial%20intelligence

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Artificial life andartificial intelligence
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Todays agenda

• Complex Adaptive Systems
• Artificial intelligence
• Artificial life
• Genetic algorithms
• Foundation
• Genetic operators
• GeneIPD

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Complexity theory
Complex adaptive systems exhibit properties that
emerge from local interactions among many
heterogeneous agents mutually constituting their
own environment
Boids
A model of the Internet
The Santa Fe Institute
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Complex Adaptive Systems

• A CAS is a network exhibiting aggregate
  properties that emerge from primarily local
  interaction among many, typically heterogeneous
  agents mutually constituting their own
  environment.
• Emergent properties
• Large numbers of diverse agents
• Local and/or selective interaction
• Adaptation through selection
• Endogenous, non-parametric environment

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What is the mind?

• Marionettes (ancient Greeks)
• Hydraulics (Descartes)
• Pulleys and gears (Industrial Revolution)
• Telephone switchboard (1930s)
• Boolean logic (1940s)
• Digital computer (1960s)
• Hologram (1970s)
• Neural networks (1980s - ?)

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Bottom-up vs top-down approach
Intelligence
Life
model this
model these
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Definition of life

• No universally agreed definition of life.
• Typical features
• Self-organization
• Emergence
• Autonomy
• Growth
• Development
• Reproduction
• Adaptation
• Responsiveness
• Evolution
• Metabolism

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Artificial life

• By the middle of this century, mankind has
  acquired the power to extinguish life on Earth.
• By the middle of next century, it will be able
  to create it. Of the two it is hard to say which
  places the largest responsibility on our
  shoulders

Chris Langton
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Artificial life

• Theoretical biology
• Artifactual (man-made), not unreal
• Bottom up, not top down
• Synthesis, not analysis
• Leverages emergence
• The artificial in Artificial Life refers to
  the component parts, not the emergent processes.
  If the component parts are implemented correctly,
  the processes they support are genuineevery bit
  as genuine as the natural processes they
  imitate. (Langton)

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Boids

• Craig Reynolds (1987) work on flocking behavior
• Virtual birds with basic flight capability
• 3 rules
• (i) collision avoidance avoid collisions with
  nearby flock-mates
• (ii) velocity matching attempt to match
  velocity with nearby flock-mates.
• (iii) flock centering attempt to stay close to
  nearby flock-mates
• Each boid is a basic unit that sees only its
  nearby flock-mates and flies according to the
  3 rules.

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Boids (cont.)
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Boids (cont.)

• Result boids flocked and flew as a cohesive
  group. When obstacles appeared in their way they
  spontaneously split into 2 subgroups, without
  central guidance, and rejoined after clearing
  obstruction.
• Illustrate basic principles of Alife systems
• Large number of simple elemental units
• Units interacting with nearby neighbors with no
  central controller
• High-level emergent phenomena from low level
  interactions

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Novelty
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Genetic algorithms

• Genetic algorithms (GAs) are an optimization
  technique
• GAs are based on Darwins theory of evolution
• Genetic algorithms combine search algorithms with
  the genetics of nature.  
• Invented by John Holland in mid 70s

Charles Darwin 1809-1882
John Holland
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Biological terminology

• Cell nucleus contain the genetic information
• Genetic information is stored in the chromosomes
• The chromosome is divided in parts genes
• The different settings of the genes for one
  property is called allele
• Every gene has an unique position on the
  chromosome locus

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Reproduction

• During sexual reproduction, crossover occurs
• This recombination of chromosomes become the new
  individual
• Hence genetic information is shared between the
  parents in order to create new offspring
• During reproduction errors occur mutation

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Natural selection

• The Origin of Species Preservation of
  favourable variations and rejection of
  unfavourable variations.
• There are more individuals born than can survive,
  so there is a continuous struggle for life.
• Individuals with an advantage have a greater
  chance to survive survival of the fittest.

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How does it works?
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Why genetic algorithms?

• If nature can do it, why cant computers?
• Power of evolution to solve optimization problems
• Particularly well suited for hard problems where
  little is known about the underlying search space

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Selection

• Main idea better individuals get higher chance
• Roulette-wheel sampling gives more fit
  individuals better chance

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1-point crossover

• Sexual reproduction is performed by mixing genes
  from the parents
• With a certain probability, we cross over some
  couples.
• Choose a random point on the two parents
• Split parents at this crossover point
• Create children by exchanging tails

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Mutation

• Occurs at the isolated gene level within a
  chromosome
• Can result in changes, both positive and
  negative, in the fitness of an individual
• When positive, can help increase individuals
  chance of being selected to be a parent of next
  generation

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A standard genetic algorithm

• Start with a randomly generated population of n
  chromosomes (genotypes)
• Calculate the fitness developed by each
  chromosomes in the population
• Repeat the following steps until n offspring have
  been created
• Select a pair of parent chromosomes from the
  current population, the probability of selection
  being an increasing function of fitness
• Crossover the pair, with probability pc
  (crossover rate), at one or two randomly chosen
  points, to produce two new offspring
• Mutate the offspring at each locus with
  probability pm (mutation rate), and place the
  resulting chromosomes in the new population
• Replace the current population with the new
  population
• Go to step 2 (next generation)

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Iterated Prisoners Dilemma

• Cohen, Riolo, and Axelrod. 1999. The Emergence
  of Social Organization in the Prisoner's Dilemma
  (SFI Working Paper 99-01-002)
• http//www.santafe.edu/research/publications/wpab
  stract/199901002
• In The Evolution of Cooperation, Robert Axelrod
  (1984) created a computer tournament of IPD
• cooperation sometimes emerges
• Tit For Tat a particularly effective strategy

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Prisoners Dilemma Game

• Column
• C D
• C 3,3 0,5
• Row
• D 5,0 1,1

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One-Step Memory Strategies
Strategy (i, p, q)
i prob. of cooperating at t 0 p prob. of
cooperating if opponent cooperated q prob. of
cooperating if opponent defected
C
p
Memory
C
D
q
C
D
D
t
t-1
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The Four Strategies
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TFT meets ALLD
Cumulated Payoff
p1 q0
0
1
1
1
3



Row (TFT)
i1
C
D
C
Column (ALLD)
D
i0
1
5
1
1
8



p0 q0
t
0
1
2
3
4
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Payoffs for 4x4 strategies
Own strategy Others strategy Others strategy Others strategy Others strategy
Own strategy ALLC TFT ATFT ALLD
Own strategy pay/move sum pay/move sum pay/move sum pay/move sum
ALLC 3333 12 3333 12 0000 0 0000 0
TFT 3333 12 3333 12 0153 9 0111 3
ATFT 5555 20 5103 9 1313 8 1000 1
ALLD 5555 20 5111 8 1555 16 1111 4
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Genetic encoding for IPD model

• For each of the possible historical cases
  encode a move.

History
Future
Player 1
C D D C D C D D D C C D D C C C D C
C
Player 2
DD D C D C D D C C C C C C C D C C
D
Sliding window (memory depth 3)
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Genetic encoding for IPD (cont.)

• Memory depth 2

Two-rounds memory
Second phantommove
D
C
D
C
D
Initialphantommove
C
D
D
D
C
D
D
C
D
C
D
C
D
C
D
C
D
C
D
C
D
C
D
C
C
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
1
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GeneIPD

• Do cooperation emerge in a spatial world?
• Do defection emerge in a soup?
• Is this always the case?
• How does the crossover and mutation rates affect
  the simulation?

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Definition of life

• What is life? Is artificial life possible?
• Webster dictionary
• 1a. The quality that distinguishes a vital and
  functional being from a dead body or purely
  chemical matter.
• 1b. The state of a material complex or individual
  characterized by the capacity to perform certain
  functional activities including metabolism,
  growth and reproduction

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Life versus Intelligence

• Life is Islands of information that persist and
  reproduce.
• Intelligence is The use of information to
  persist and reproduce.

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Alternative Crossover Operators

• Performance with 1-point crossover depends on the
  order that variables occur in the representation
• More likely to keep together genes that are near
  each other
• Can never keep together genes from opposite ends
  of string
• This is known as Positional Bias
• Can be exploited if we know about the structure
  of our problem, but this is not usually the case

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n-point crossover

• Choose n random crossover points
• Split along those points
• Glue parts, alternating between parents
• Generalization of 1 point (still some positional
  bias)

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Uniform crossover

• Assign 'heads' to one parent, 'tails' to the
  other
• Flip a coin for each gene of the first child
• Make an inverse copy of the gene for the second
  child
• Inheritance is independent of position

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Genetic encoding for IPD (cont.)
Tit-For-Tat

• 1 move remembered If CC (case 1), then C If
  CD (case 2), then D If DC (case 3), then C If
  DD (case 4), then D
• Can be encoded as the string CDCD or 1010
• For two possible moves remembered -gt 16 (4 x 4)
  possibilities (16 bits string)
• For three possible moves remembered -gt 64 (4 x 4
  x 4) possibilities (64 bits string)

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Genetic encoding for IPD (cont.)

• But this is not enough, as the strategy requires
  results from previous games
• Extra genes to encode hypothetical moves 1 move
  memory 4 1 5 bits 2 moves memory 16 3
  19 bits 3 moves memory 64 7 71 bits
• Number of potential strategies for 3 moves
  history is in the order of 1021