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From arbitrary Turing Machines to Rule 110 in Python

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Rule 110

This is a Python implementation of Rule 110 as a universal cellular automata. It is based on Matthew Cook's papers:

  • Cook, Matthew. (2004). Universality in Elementary Cellular Automata. Complex Systems. 15. link
  • Cook, Matthew. (2009). A Concrete View of Rule 110 Computation. Electronic Proceedings in Theoretical Computer Science. 1. link

What is going on here?

For now - pretty much everything interesting is in the turing_post.py file. It first defines a few Turing Machines used as the examples:

  • HaltingBracketMachine, which halts if the input tape contains a correctly matching brackets.
  • HaltingIs1MachineStartSymbol which halt if the input tape starts with a symbol 1
  • HaltingIs1Machine, the same as above, but with a slightly different implementation

Then it defines all the required machinery to run Turing Machines and unit-tests these above.

When we're sure that they work, it applies a sequence of transformations, starting from arbitrary TMs and goingh through:

  • TMs over ternary alphabet
  • TMs over binary alphabet
  • Tag systems
  • Cyclic tag systems
  • Cellular automata

The last step uses diagrams exported from Cook's for setting up the tape for CAs. Diagrams are included in the blocks/ directory.

Results

Unfortunately, there is an exponential blowup when going from tag systems to cyclic tag systems, which makes it unusable even for such simple machines as HaltingIs1MachineStartSymbol. Cook's describes an alternative approach, which does not have this problem, but it is not yet implemented.

To test the correctness, I wrote a simple rule 110 interpreter in C, in process.c, but even with its help, I couldn't get the machines to terminate on non-trivial examples.

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