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|Artificial Life
|
|  Dave Ackley
|  Computer Science
|  University of New Mexico
|                    ackley cs. unm. ed u
|
|  Visiting UCSD Cognitive Science 
|  2001-2002     ackley hci. ucsd. ed u
|
|    http://ackleyshack.com/
|
__________________________________
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| Something happened July 19, 2001
|
|(animation)
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| Code Red (v2 w/randomized seed)
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|- Infected >359,000 hosts in 14 hours
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|- Peak infection rate: 2,000 hosts/min
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|- Global network routing instabilities
|    
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| Is Code Red alive?
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|Outline
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|- What is life
|
|- Artificial life
|  - science & engineering
|
|- What we can learn
|
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|What is life?
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|- Is that the right question?
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|- What are we looking for?  
|    A definition?
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|What is (the definition of) life?
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| Webster's Unabridged (1913):
|
|  1. The state of being which begins with
|  generation, birth, or germination, and ends
|  with death; also, the time during which this
|  state continues; that state of an animal or
|  plant in which all or any of its organs are
|  capable of performing all or any of their
|  functions; -- used of all animal and
|  vegetable organisms. ...
|
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|What is life?
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|- Can we find necessary & 
|  sufficient conditions?
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|- What characteristics do all
|  living things share?   
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|Characteristics of life
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|Life is that which ...?
|- Grows                
|- Consumes to survive  
|- Reproduces itself    
|- Heals itself         
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|What is life?
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|- No firm "scientific definition"
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|- Is "being life" an all-or-none
|  proposition?
|
|- Big umbrella:
|  "Dynamically maintained patterns"
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|What is (the definition of) life?
|Life is:
|
|"a self-sustaining chemical system capable 
| of undergoing Darwinian evolution." 
|                     (Gerald Joyce)
| 
| "a self-organized non-equilibrium system 
|  such that its processes are governed by 
|  a program that is stored symbolically and 
|  it can reproduce itself, including the 
|  program."          (Lee Smolin)
|
| "nature's way of preserving meat." 
|
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|Artificial life ("alife", "AL")
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|- Biology: Compared to what?
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|- `Life as it is' vs `Life as it could be'
|       
|- The weak claim for AL: Computational
|   systems can be models of living systems
|
|- The strong claim for AL: Computational
|   systems can be living systems
|
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|Artificial life
|
| - Strong claim advocates:
|   = Talk about "systems"
|   = Focus on building
|   = Seek things that work
|
| - Weak claim advocates:
|   = Talk about "models"
|   = Focus on understanding
|   = Seek things that predict
|
| - The "World In A Box" methodology
|

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|Artificial life 
|
|- The empirical study of 
|   evolutionary systems via
|   computational modelling
|
|- Relationships:
|  Biology, Cognitive science,
|  Artificial intelligence, Economics
|  
|(Also: Cultural evo;evo language; 
| ethology; epistemology...)
|
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|Artificial life: How to play
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|- Focus on discovering consequences
|  of evolutionary processes
|
|- Evolution needs:
|  (1) Heritable traits
|  (2) Source of variation
|  (3) Source of differential survival
|                     "fitness"

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|Artificial life: How to play
|
|Typical alife responses -
|(1) Heritable traits:
|  - specified directly by the creator
|(2) Source of variation:
|  - specified directly by the creator
|(3) Source of differential survival
|  - specified indirectly by the creator
|    via the "world" the creatures inhabit
|
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|Artificial life: How to play
|
|- Points to note:
|  = Full of outrageous simplifications
|     (and yet)
|  = Myriads of "parameters", with
|    few motivations
|     
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|Artificial Life: Examples
|
|- Learning from evolution
|     (e.g., Ackley & Littman, 1992)
|
|- Evolution of communication
|     (e.g., Ackley & Littman, 1994)
|
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|Learning from evolution
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|- Learning and evolution:
|  Mechanisms of adaptation
|    
|- Learning algorithms depend on
|    supervision/reinforcement/feedback
|
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|Learning from evolution
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|- Less feedback, generally harder to learn
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|- Least feedback: Death
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|- How to learn from death?
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|Learning from evolution: A model
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|- Need both behavior ("what to do")
|    and evaluation ("how am I doing")
|
|- "Genes" specify evaluation (fixed
|    for life) and initial behavior 
|    (modifiable by learning)
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|- If evaluation score increases, 
|    reward previous action, and vv
|
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|Learning from evolution: A model
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|- World is 100x100 grid
|- Learning and evolving agents
|- Finite state handcoded predators
|- Growing grass supplies food
|- Growing trees supplies shelter
|

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|Learning from evolution: A model
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|Most typical outcome: 
|- Agent populations quickly extinct
|
|Result:
|- L+E populations survive more often
|  compared to E-only, L-only, Neither
|
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|Learning from evolution: A model
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|Other observations:
|- Predator-prey oscillations
|- Long-term adaptation
|




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|Learning from evolution
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|Other observations:
|- Baldwin effect
|- Shielding
|
|- Multilevel models
|- Scaling in time
|
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|Artificial Life: Examples
|
|- Learning from evolution
|     (e.g., Ackley & Littman, 1992)
|
|- Evolution of communication
|     (e.g., Ackley & Littman, 1994)
|
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|Evolution of communication
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|- Also, e.g., Batali (1994, 1998), Hutchins &
|  Hazelhurst (1995), Werner & Todd (1997), ...
|
|- Beyond the isolated individual
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|- Evolution of cooperation
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|- Would an agent ever `speak truth' if
|    it only benefited the listener?
|
|- Assigning meaning to arbitrary signals
|

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|Evolution of communication
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|- Spatial levels: Individual, local, global 
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|- Reproduction & communication is local
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|- `Migration' mixes nearby local populations
|



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|Evolution of communication
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|- Communication based on arbitrary signals
|   can evolve and stabilize even when it
|   provides no benefit for the individual speaker. 
|
|- Key is partial alignment of communication
|    range and reproductive range
|
|- A variety of `parasites' may arise
|
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|Outline
|
|- What is life
|
|- Artificial life
|  - science & engineering
|
|- What we can learn
|
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|What we can learn
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|- May need less than we think
|
|- Unexpected connections between
|  phenomena, between local and
|  global views, and short/small
|  and long/large
|
|- "Physical intuitions"
|  for evolutionary systems
|