1 / 51

Digital Universes

Digital Universes. BARAK NAVEH, www.cs.bgu.ac.il/~barnav. < Previous. Next >. Evolution in Other Contexts. Life on Earth is a product of evolution by natural selection operating in the medium of carbon chemistry .

gyula
Download Presentation

Digital Universes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Digital Universes BARAK NAVEH, www.cs.bgu.ac.il/~barnav < Previous Next >

  2. Evolution in Other Contexts • Life on Earth is a product of evolution by natural selection operating in the medium of carbon chemistry. • However, in theory, evolution is not limited to Earth, nor to carbon chemistry. • Just as it may occur on other planets, it may also operate in other media, such as the medium of digital computation.

  3. Carbon-Based Organization • The organizationgenerated by evolution spans about twelve orders of magnitude of scale. • from the molecular to the ecosystem level.

  4. Evolution in Organic Medium • Organic lifeuses energy and organizes matter. • Evolution on Earth has organized matter from the molecular level up to the ecosystem level.

  5. Evolution in Digital Medium • Can we use evolution to develop such organization? • Can life use CPU-timetoorganize memory? • Can we use evolution to synthesize digital life?

  6. What isLife? • No clear definition. • We will regard to an object as alive if it is • Self-replicating • Capable of open-ended evolution

  7. Tom Ray’sTierra Project

  8. The Creatures • Self-replicating machine code programs. • Why machine code? (most natural to the machine) • Machine instructions remind us of amino acids because they are “chemically active”. (actively manipulate bits, bytes, CPU registers) • The “genome” of a creature is the sequence of its machine instructions.

  9. The Environment – Tierra VM • Why Virtual Machine? • Avoid the threat of evolving hostile code such as viruses or worms. • Von Neumann type machine languages are fragile, any mutation or recombination event is almost certain to completely break program. • To make it especially hospitable to synthetic life. • Tierra is a (simulated) parallel computer • with a processor for each creature.

  10. Each CPU • Contains • 2 address registers + 2 numeric registers • Small Stack + Stack pointer • Instruction pointer • Flags register to indicate error conditions • Performs fetch-decode-execute-inc(IP) cycle • Has a simple instruction set for • Arithmetics, bit manipulation • Moving data between registers and RAM • Control “instruction pointer” (IP) • Computations are probabilistic • Mutations occur at some low rate 

  11. The Tierran Language • 32 instructions represented by five bits, operands included. • Numeric operands eliminated • Instruction set need not include all possible integers. • CPU registers and stack are the only operands of instructions. • Bit flipping and shifting is used to synthesize numbers. • Errors that cause instructions to fail make them have no effect.

  12. Template Addressing • Numeric operands are normally used to specify addresses, such as absolute or relative addresses for jmp instruction. • Numeric operands were eliminated • (another method is needed) • In Tierra, the jmp instruction uses a template instead of an absolute or relative address.

  13. Template Addressing • Templates are “borrowed” from molecular biology. • Molecules “address” one another by having complementary shapes. • Templates are complementary patterns of zeros and ones. • Templates are built from two kinds of nop instructions: nop0 and nop1

  14. Template Addressing • The instruction sequence: jmpnop0nop0nop1 • causes execution of the program to jump to the nearest occurrence of the instruction sequence: nop1nop1nop0 • Why use complementarity? • so that the jmpwill never jump to itself.

  15. Instruction Set

  16. Memory Allocation • The Tierran computer operates on a block of RAM of the real computer, referred to as the “soup”. • The soup consisted of 60,000 bytes, which can hold 60,000 Tierran machine-instructions. • Each “creature” occupies some area in the soup. • Memory is circular.

  17. The Soup

  18. Cellularity • The cell membrane is defining its limits and preserving its structural integrity. • In digital organisms we need an analog to cell membrane in order to prevent them from demolishing one another easily when they come into contact

  19. Cellularity (cont.) • Each Tierran creature has exclusive write privileges within its own memory • A creature may examine the code of another creature, and even execute it, but it can NOT overwriteit.

  20. Cellularity and Division • Creature has write privileges to: • The memory block it is born with (mother cell). • The memory block it may allocate using mal instruction (daughter cell), which may be used to grow or to reproduce into. • Upon creature divide instruction: • The mother cell loses write privileges on daughter cell’s. • The daughter cell is given its own CPU and can allocate its own second memory block.

  21. The Slicer • Time sharing approximates parallelism. • The number of instructions to be executed in each slice may be set in proportion to the size of the creature being executed, raised to a “slicer-power”. • The power determines if selection favors large or small creatures • power < 1: favors small • power = 1: size neutral • power < 1: favors large

  22. Mortality - The Reaper • At birth, processes enter the bottom of the Reaper queue. • When the memory is full, the Reaper kills processes at the top of the queue. • Memory allocated to the dead process is reclaimed. • The code of a dead process is NOT removed from the soup.

  23. The Reaper (cont.) • When a process generates an error, it moves one position up the Reaper queue. • Successful execution of divide or mal moves the process one position down. • Overall effect: • Flown creatures rise to queue top and die. • Vigorous creatures have a greater longevity. • The probability of death increases with age.

  24. Mutations • Two kinds of mutations of machine instructions: • a single bit of an instruction is flipped • random replacements - the affected instruction is replaced by one of the 32 instructions in the set, chosen at random • Mutations occur when: • a process is born • code is copied from place to place • any time at random (cosmic ray)

  25. Gene Splicing • There are three classes of splicing: • Crossover • Insertion • Deletion • Each class can occur in two ways: • Anywhere in the genome • Only at “segment boundaries”, marked by templates • Gene splicing is applied to a daughter process at the time of birth

  26. Flaws • Flaws were originally conceived of as being analogous to metabolic reactions gone wrong, or producing side products • Flaws are “intentional” errors in the operations of the machine instructions • Most flaws are errors of magnitude + or – 1 • Increment/decr may add/sub 2 or 0 instead of 1 • Instructions shifting or rotating bits in registers may shift the bits one place too much or too little

  27. Tierra System • Self replicating individuals • Genetic alterations • Natural selection • Co-evolution Results

  28. Ancestor 0080aaa 1111 find 0000 (start) -> bx find 0001 (end) -> ax calculate size -> cx Self-examination 1101 allocate daughter -> ax call 0011 (copy procedure) cell division jump 0010 Reproduction Loop 1100 save registers to stack 1010 move [bx] -> [ax] decrement cx if cx == 0 jump 0100 increment ax & bx jump 0101 1011 restore registers return 1110 Copy Procedure (coded by human)

  29. Ancestor 0080aaa 1111 find 0000 (start) -> bx find 0001 (end) -> ax calculate size -> cx Self-examination 1101 allocate daughter -> ax call 0011 (copy procedure) cell division jump 0010 Reproduction Loop 1100 1100 save registers to stack 1010 move [bx] -> [ax] decrement cx if cx == 0 jump 0100 increment ax & bx jump 0101 1011 restore registers return 1110 Copy Procedure

  30. Mutant 1111 find 0000 (start) -> bx find 0001 (end) -> ax calculate size -> cx Self-examination 1101 allocate daughter -> ax call 0011 (copy procedure) cell division jump 0010 Reproduction Loop 1110 save registers to stack 1010 move [bx] -> [ax] decrement cx if cx == 0 jump 0100 increment ax & bx jump 0101 1011 restore registers return 1110 Copy Procedure

  31. Parasite 0045aaa 1111 find 0000 (start) -> bx find 0001 (end) -> ax calculate size -> cx Self-examination 1101 allocate daughter -> ax call 0011 (copy procedure) cell division jump 0010 Reproduction Loop 1110

  32. Ancestor 0080aaa Parasite 0045aaa Self-exam Self-exam 1111 find 0000 (start) -> bx find 0001 (end) -> ax calculate size -> cx 1111 find 0000 (start) -> bx find 0001 (end) -> ax calculate size -> cx ReproductionLoop Reproduction Loop 1101 allocate daughter -> ax call 0011 (copy procedure) cell division jump 0010 1101 allocate daughter -> ax call 0011 (copy procedure) cell division jump 0010 1100 1110 Copy Procedure save registers to stack 1010 move [bx] -> [ax] decrement cx if cx == 0 jump 0100 increment ax & bx jump 0101 1011 restore registers return 1110

  33. Parasite 0045aaa 0080gai Self-exam 1111 find 0000 (start) -> bx find 0001 (end) -> ax calculate size -> cx Reproduction Loop allocate daughter -> ax call 0011 (copy procedure) cell division jumpb 0000 1100 Copy Procedure 1010 move [bx] -> [ax] decrement cx if cx == 0 jumpb 1100 increment ax & bx jumpb 0101 1110 Self-exam 1111 find 0000 (start) -> bx find 0001 (end) -> ax calculate size -> cx Reproduction Loop 1101 allocate daughter -> ax call 0011 (copy procedure) cell division jump 0010 1110 Hyper Parasite!

  34. 1010 move [bx] -> [ax] decrement cx if cx == 0 jumpb 110 increment ax & bx jumpb 0101 111 ?? 110 find 001 (start) -> bx find 000 (end) -> ax calculate size -> cx 0061acg Self-examination Social Hyper-parasite allocate daughter -> ax call 001 (copy procedure) cell division jumpb 010 Reproduction Loop 1100 Copy Procedure 1010 move [bx] -> [ax] decrement cx if cx == 0 jumpb 110 increment ax & bx jumpb 0101 111

  35. Other Results • Immunity to parasites • Circumvention of immunity to parasites • Cheaters (e.g., 0027aab) • Abuse the cooperation of social hyper-parasites • Novel forms of self examination • Optimization • Size decrease • Loop unrolling • Emergence of Ecology

  36. More Info www.isd.atr.co.jp/~ray/tierra/index.html It’s life Jim, but not as we know it (Dr. McCoy, Star Trek)

  37. Related Works • Network Tierra (T. Ray) • Connect many machines together to form a bigger “soup”. • “Avida” (Adami, Brown, 94), similar idea but • On a grid (locality) • I/O and (limited) ability to train organisms to perform functions • Active research • “Amoeba” (Pargellis, 96), similar idea • Simpler instruction set • Spontaneous emergence of self-replicators

  38. Related Works (cont.) • “Physis” (A. Egri-Nagy, ‘03) • Evolves both: VM and programs • Encodes the computer together with the program • “String Based Tierra” (K. Sigiura, ‘03) • Encodes programs into strings • Uses reg-expr rules to match-and-substitute (to compute) • Rules are strings as well • Evolve programs and their rules as a single individual

  39. Open Challenges • Why creature complexity has stopped increasing? • What’s limiting further development? • Over 10 years have passed: • Memory space can support x10,000 bigger soup • CPUs can crunch x100 faster • In many cases “more is different” – is it here?

  40. Demos of Other Artificial-Life Works Karl Sims Demetri Terzopoulos

  41. Evolving Virtual Creatures Karl Sims ’94 Play

  42. Evolving Artificial FishDemetri Terzopoulos ’94-’99 Evolving a Swimmer Go Fish Jack Cousto

  43. Thank You Good Luck

More Related