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Transcript of Course material – G. Tempesti gt512/BIC.html Course material will generally be available the day...
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Course material – G. Tempestihttp://www-users.york.ac.uk/~gt512/BIC.html
Course material will generally be available the day before the lecture
Includes PowerPoint slides and reading material
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Ontogenetic systems
Drawing inspiration from growth and healing processes of living organisms…
…and applying them to electronic computing systems
Phylogeny (P)[Evolvability]
Epigenesis (E)[Adaptability]
Ontogeny (O)[Scalability]
PO hw
POE hw
OE hw
PE hw
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Introduction
At the heart of the growth of a multi-cellular organism is the process of cellular division…
… aka (in computing) self-replication
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Introduction In the 50s, John von Neumann wanted to build a
machine capable of self-replication
Mark II Aiken Relay Calculator (Harvard, 1947)
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Introduction In the 50s, John von Neumann wanted to build a
machine capable of self-replication… but HOW?
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Introduction In the 50s, John von Neumann wanted to build a
machine capable of self-replicationAt the same time, Stanislaw Ulam was working
on the computer-based realization of recursive patterns: geometric objects defined recursively.
Ulam suggested to Von Neumann to build an “abstract world”, controlled by well-defined rules, to analyze the logical principles of self-replication: this world is the world of cellular automata.
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Cellular Automata (CA) Conceived by S.M. Ulam and J. von Neumann
Framework for the study of complex systems
Organized as a two-dimensional array of cells
Each cell can be in a finite number of states
Updated synchronously in discrete time steps
The state at the next time step depends of the current
states of the neighbourhood
The transitions are specified in a rule table
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Environment states
0 =
1 =
2 =
3 =
4 =
etc…
Cellular Automata (CA)
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Cellular Automata (CA)
Environment states neighbourhood
Wolfram (1-D)
Von Neumann
Moore (Life)
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Cellular Automata (CA)
Environment states neighbourhood transition rules
== ==
== ==
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Cellular Automata (CA)
Environment states neighbourhood transition rules
Configuration Initial state of the array
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Wolfram’s Elementary CA
The simplest class of 1-D CA: two states (0 or 1), and rules that depend only on nearest neighbour values. Since there are 8 possible states for the three cells in a neighbourhood, there are a total of 256 elementary CA, each of which can be indexed with an 8-bit binary number.
Rule 30
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Wolfram’s Elementary CA
Rule 30
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Invented by John M. Conway (University of Cambridge)
Popularised by Martin Gardner (Scientific American, october 1970, february 1971)
Two-dimensional CATwo states per cell: dead and aliveEight neighbours (Moore)
2D CA: Game of Life
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2D CA: Game of Life
• Birth of a cell
• Death of a cell
• Survival of a cell
• More than three neighbors• Less than three neighbors
• Two or three neighbors
• Three neighbors
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2D CA: Game of Life
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Gliders:
Glider gun:
Game of Life: the glider
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Game of Life
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Von Neumann’s CA
Environment
states = 29 neighborhood = von Neumann transition rules = 295 ~ 20M
Configuration
Initial state of the array ~ 200k cells for the constructor, > 1M for the memory tape
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Von Neumann’s Constructor
Von Neumann’s Universal Constructor (Uconst) can build any finite machine (Ucomp), given its description D(Ucomp).
Uconst
D(Ucomp)M
Ucomp
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M
Uconst
D(Uconst)
Von Neumann’s Constructor
Von Neumann’s Universal Constructor (Uconst) can build a copy of itself (Uconst’), given its own description D(Uconst).
Uconst'
D(Uconst)M'
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Von Neumann’s Constructor
Von Neumann’s Universal Constructor (Uconst) can build a copy of itself (Uconst’) and of any finite machine (Ucomp’), given the description of both D(Uconst+Ucomp).
MUconstUcomp
D(Uconst+Ucomp)
M'
D(Uconst+Ucomp)
Ucomp' Uconst'
The universal constructor is a unicellular organism.
MOTHER CELL
DAUGHTER CELL
GENOME
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Von Neumann’s Constructor Ordinary transmission states
Standard signal transmission paths (wires)
Non-excited:
Excited:
Input
InputInput
Output
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Von Neumann’s Constructor Ordinary transmission states
Property 1: Transmission of excitations with a unit delay
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Von Neumann’s Constructor Ordinary transmission states
Property 2: OR logic gate
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Von Neumann’s Constructor Confluent states
Signal synchronization Non-directional (depends on neighbor’s direction)
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Von Neumann’s Constructor Confluent states
Property 1: Introduction of double unit delay
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Von Neumann’s Constructor Confluent states
Property 2: AND gate
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Von Neumann’s Constructor Confluent states
Property 4: Fan-out
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Von Neumann’s Constructor The XOR gate
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Von Neumann’s Constructor The SR flip-flop
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Von Neumann’s Constructor
Sensitive states Construction
Ordinary or special excitation
No excitation
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Demonstration
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Demonstration
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Self-replicating CA After von Neumann, nothing much happened for
almost 30 years! Why? Probably because the hardware wasn’t
ready. In 1984, Chris Langton designed Langton’s Loop
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Langton’s Loop Environment: 8 (?) states, 5 neighbours (von
Neumann), rules designed by hand Initial configuration: 94 active cells (vs. 200k+ in
von Neumann’s Universal Constructor) Replication occurs after 151 iterations
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Langton’s Loop Aim: studying self-replication as “Artificial Life” Problem: does nothing but self-replicate
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Langton’s Loop After Langton, the loops were optimized In one case (Perrier et al.) a Turing machine was
added to the loop (but at a high cost)
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Towards functional self-replication Environment: 7+ states, 9 neighbours (Moore),
rules designed by hand Simple initial configuration, easily simulated
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Towards functional self-replication Can be extended by adding “applications” (the
complexity depends on the task)