1. Artificial Life Systems

2. Why? Seek generalizations about self replication
Address questions we can’t do with real organisms
Scale unavailable with real life
Evolution
Design solutions to computational problems

3. Beginnings Conway’s Life Core Wars (1960’s) Chris Langton’s Ant 1986
Steen Rasmussen observed the success for code war player replicating themselves (1990)
Thomas Ray writes Tierra (early 1990’s)
Digital organisms as programs (80 lines)
Chris Adami used Tierra with evolution (1992)
Avida (1993) with Charles Ofria and C. Titus Brown
Andrew Pargallis demonstrates spontaneous generation

4. Avida

5. Avida Virtual Machine Read Write heads Flow Control Instruction memory
CPU
Registers (3)
Stacks

6. Genetic language Multiple instructions sets Template matching
Instructions are opcode only
Organized into subsets
Default subset is 26 instructions
Template matching
Relative addressing
A series of instructions use to address a location
Allows insertions and deletions via mutation

7. Example Inc – increments a register by 1 Which register?
Default is BX register
If followed by nop-a instruction then increment the ax register
Head are controlled by mov-head instruction
Default: move the instruction to position of flow control.
Nops change default

8. Replication (with copy mutation)
Allocate
Divide

9. Avida world N cells each can hold one organism
World can have topologies

10. Organism Structures
# --- Setup --- h-alloc # Allocate extra space at the end of the genome to copy the offspring into. h-search  # Locate an A:B template (at the end of the organism) and place the Flow-Head after it nop-C # nop-A # mov-head # Place the Write-Head at the Flow-Head (which is at beginning of offspring-to-be). nop-C # [ Extra nop-C commands can be placed here w/o harming the organism! ]
# --- Copy Loop --- h-search # No template, so place the Flow-Head on the next line code h-copy # Copy a single instruction from the read head to the write head (and advance both heads!) if-label # Execute the line following this template only if we have just copied an A:B template. nop-C # nop-A # h-divide #     ...Divide off offspring! (note if-statement above!) mov-head # Otherwise, move the IP back to the Flow-Head at the beginning of the copy loop. nop-A # End label.

11. A creature Search # 1: Find organism end. Nop-C # 2: - Match CD:AB
Nop-D
Push-Prev # 5: Move end position to Stack-A
SetMemory # 6: Place FLOW-head in memory space for offspring
Nop-A # Memory space labeled Nop-A Nop-A Nop-A
Head-Move # 7: Move Write head to flow head position
Nop-C # 8:
Followed by lots of nop-c
Nop-C # 8:
Search # 9: Drop flow head at start of copy loop
Inst-Read # 10:
Inst-Write # 11:
Head-Push # 12: Get current position of...
Nop-C # 13: - Read-Head
If-Equal # 14: Test if we are done copying...
Divide # 15: ...If so, divide.
Head-Move # 16: ...If not, continue with loop.
Nop-A # 17:
Nop-B

13. Some crtisms Lack of explicit theoretical grounding
Predefined organism structure
Restricted ecological interactions
No competition for matter and energy
Evolving a self-reproductive algorithm

14. Framsticks

15. Developed since 1996 (Komosinski and Ulatowski) 3D worlds
Evolution driven by a complex environment
Bodies are “sticks and joints”
World simulation has basic physics (e.g. friction)
Documentation is an issue!

18. Body specification Tree structure: X represents a line segment (stick)
XXX is three segments in line
XXX(XX) is a tree structure
Rotations can be added with an R modifier or commas
Other modifiers plus parameter setting in string

19. Brain Neural nets Basic AI type neurons Function modules
Sinusoidal generators
Random generators
Thresholding neurons
Delay neurons
Differentiating neurons

20. Receptors and Effectors
G – orientation and space
T – touch
S – detection of energy (smell)
Effectors
Bending and rotation
Flexors and extensors

21. Brain encoding Neurons are coded into the framsticks body definition
The brackets are motor neurons, p is the strength property
[0:0][-1:1][-1:1]
Chain of neurons
[N][Sin,f0:0.02-1:0.1]…
-1:0.1 is a connection weight 0.1 to the pervious neuron

22. Brain encoding(cont.) X[T][G][S][-3:1, -2:1, -1:1]
Three sensors feeding a neuron

23. Genetics Encoding shown are f1 f0 is primitive machine level
There are multiple levels - f4 supports evolution operations
Each encoding has a set of genetic operators

24. Other features FrameScript for extensions
OO with methods for system events
onBorn, onKill, onCollision
Fuzzy controls
GUI design tools
Analysis tools (e.g. Brain Analysis)
Brain Analysis allows probes

25. Experiments Pendulum-body with fuzzy conrol
Genotype-phenotype encoding
Predator-prey showed arms race and stable population in evolution
Semiotics