Below is a Turing machine implementation based on the example in Understanding Computation by Tom Stuart. The Turing machine is an entertaining thought exercise that makes computer science feel a bit like poetry. Most things I learn on about computers on a daytoday basis are decidedly unpoetic.
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This machine contains objects for a tape (Tape
), rules for a machine to follow (Rule
), and an object representing the state of the Turing machine itself (DeterministicTuringMachine
).
Incrementing binary numbers ¶
Given the appropriate set of rules, this machine can perform general computing tasks. In the book, the rules for incrementing a binary number are used as an example.
We start with the number 10111
(A.K.A, 23), which we’d like to increment by 1 to get 11000
(A.K.A., 24). To begin we set the tape with the number we’d like to increment with the read head of the tape resting on rightmost digit of the binary number:
# Tape looks like: 1011(1)
# Where () represents the read/write head
t = Tape(left=[1,0,1,1], middle=1)
This machine will have three available machine “states” that help to define the rules for the Turing machine to follow. When the machine is in a particular state, and encounters a particular condition (i.e., the read head is over a particular number) it will follow a particular rule – that is, it will write either a 1
or a 0
, move the read head either LEFT
or RIGHT
, and, possibly, change machine state. These rules are based on machine state in combination with a read condition.
The machine will start in state 1
. When the machine enters into one of the accept_states
, the machine will stop processing. The only accept_state for this machine is 3
.
# Availale states in this example are 1, 2, 3
initial_state = 1
m = DeterministicTuringMachine(
state=initial_state, tape=t, accept_states=[3], rules=[
# if the state is 1 and the read head is ... etc
Rule(state=1, head='0', next_state=2, write='1', move=RIGHT),
Rule(state=1, head='1', next_state=1, write='0', move=LEFT),
Rule(state=1, head=BLANK, next_state=2, write='1', move=RIGHT),
# if the state is 2 and the read head is ... etc
Rule(state=2, head='0', next_state=2, write='0', move=RIGHT),
Rule(state=2, head='1', next_state=2, write='1', move=RIGHT),
Rule(state=2, head=BLANK, next_state=3, write=BLANK, move=LEFT)
]
)
If we call the step
method of the state machine we can trace how it follows rules.
>>> m.tape
1011(1)
>>> m.step()
>>> print(m.state)
1
>>> print(m.tape)
101(1)0
Since it was in state 1
and the read head was over a 1
it followed rule Rule(state=1, head='1', next_state=1, write='0', move=LEFT)
– it wrote 0
in its current location, it moved the read head LEFT
, and stayed in the 1
state. Since the state is still 1
and the read head is onceagain over a 1
, the same rule will be followed again:
>>> m.tape
101(1)0
>>> m.step()
>>> print(m.state)
1
>>> print(m.tape)
10(1)00
Calling the run
method of the machine will continue to follow the defined rules until state 3
is reached:
>>> m.run()
>>> print(m.state)
3
>>> print(m.tape)
1100(0)_
Turing machines are magic, I guess is what I’m saying.
Another rather poetic way of understanding lowlevel computing: https://tomorrowcorporation.com/humanresourcemachine
It’s a game where you automate your way through menial tasks in the mail room at a mega corporation. The catch: you have to use something resembling assembly language to implement the automation. It’s fun and challenging.
Comment by mmodell — Tue 20170704 03:53:51 AM
Ah, you’ve mentioned this before! It does look pretty cool. I’ve played World of Goo which looks to be by the same folks and really enjoyed it