This document discusses decidable, semi-decidable, and undecidable problems in terms of Turing machines. It provides examples of each: - Decidable problems are those where a Turing machine halts on every input and provides a yes or no answer. Examples given include checking if a number is prime or if a string is in a regular language. - Semi-decidable problems are ones where a Turing machine halts on accepting inputs but may loop on rejecting inputs. Recognition of context-free languages is provided as an example. - Undecidable problems have no Turing machine that halts on every input. Examples mentioned include checking ambiguity of context-free grammars or equivalence of

1. CHAPTER TWO
Turing
Decidability

2. Decidable
 A language is called Decidable or Recursive if there is a Turing
machine which accepts and halts on every input string w. Every
decidable language is Turing-Acceptable.

3.  For a decidable language, for each input string, the TM halts either at
the accept or the reject state as depicted in the following diagram −

4. Generally A problem is said to be Decidable if we can always construct a
corresponding algorithm that can answer the problem correctly.
 We can intuitively understand Decidable problems by considering a simple
example.
 Suppose we are asked to compute all the prime numbers in the range of
1000 to 2000. To find the solution of this problem, we can easily devise an
algorithm that can enumerate all the prime numbers in this range.
 Now talking about Decidability in terms of a Turing machine, a problem is
said to be a Decidable problem if there exists a corresponding Turing
machine which halts on every input with an answer- yes or no.
 It is also important to know that these problems are termed as Turing
Decidable since a Turing machine always halts on every input, accepting
or rejecting it.

5. Example 1
Find out whether the following problem is decidable or not −
Is a number ‘m’ prime?
Solution
Prime numbers = {2, 3, 5, 7, 11, 13, …………..}
Divide the number ‘m’ by all the numbers between ‘2’ and
‘√m’ starting from ‘2’.
If any of these numbers produce a remainder zero, then it goes
to the “Rejected state”, otherwise it goes to the “Accepted
state”. So, here the answer could be made by ‘Yes’ or ‘No’.
Hence, it is a decidable problem.

6. Example 2
Given a regular language L and string w, how can we check if w ∈ L?
Solution
Take the DFA that accepts L and check if w is accepted

7. Some more decidable problems are −
 Does DFA accept the empty language?
 Is L1 ∩ L2 = ∅ for regular sets?
Note −
 If a language L is decidable, then its complement L' is also decidable
 If a language is decidable, then there is an enumerator for it.

8. Semi- Decidable Problems
Semi-Decidable problems are those for which a Turing machine halts on
the input accepted by it but it can either halt or loop forever on the input
which is rejected by the Turing Machine.
Such problems are termed as Turing Recognizable problems.
Examples – We will now consider few important Decidable problems:
 Are two regular languages L and M equivalent?
We can easily check this by using Set Difference operation.
L-M =Null and M-L =Null.
Hence (L-M) U (M-L) = Null, then L,M are equivalent.

9.  Membership of a CFL?
We can always find whether a string exists in a given CFL by using an
algorithm based on dynamic programming.
 Emptiness of a CFL
By checking the production rules of the CFL we can easily state
whether the language generates any strings or not.

10. Undecidable Language
 For an undecidable language, there is no Turing Machine which
accepts the language and makes a decision for every input
string w (TM can make decision for some input string though).
 A decision problem P is called “undecidable” if the language L of all
yes instances to P is not decidable.
 Undecidable languages are not recursive languages, but sometimes,
they may be recursively enumerable languages.
 The problems for which we can’t construct an algorithm that can
answer the problem correctly in finite time are termed as Undecidable
Problems.
 These problems may be partially decidable but they will never be
decidable.
 That is there will always be a condition that will lead the Turing
Machine into an infinite loop without providing an answer at all.

11.  We can understand Undecidable Problems intuitively by
considering Fermat’s Theorem, a popular Undecidable Problem which
states that no three positive integers a, b and c for any n>2 can ever satisfy
the equation: a^n + b^n = c^n.
 If we feed this problem to a Turing machine to find such a solution which
gives a contradiction then a Turing Machine might run forever, to find the
suitable values of n, a, b and c.
 But we are always unsure whether a contradiction exists or not and hence
we term this problem as an Undecidable Problem.

13. Examples – These are few important Undecidable Problems:
 Whether a CFG generates all the strings or not?
As a CFG generates infinite strings, we can’t ever reach up to the last string and
hence it is Undecidable.
 Whether two CFG L and M equal?
Since we cannot determine all the strings of any CFG, we can predict that two
CFG are equal or not.
 Ambiguity of CFG?
There exist no algorithm which can check whether for the ambiguity of a CFL.
We can only check if any particular string of the CFL generates two different
parse trees then the CFL is ambiguous.

14.  Is it possible to convert a given ambiguous CFG into corresponding
non-ambiguous CFL?
It is also an Undecidable Problem as there doesn’t exist any algorithm
for the conversion of an ambiguous CFL to non-ambiguous CFL.
 Is a language Learning which is a CFL, regular?
This is an Undecidable Problem as we can not find from the
production rules of the CFL whether it is regular or not.

15. Example
 The halting problem of Turing machine
 The mortality problem
 The mortal matrix problem
 The Post correspondence problem, etc.
Turing Machine Halting Problem
Problem − Does the Turing machine finish computing of the string w in a
finite number of steps? The answer must be either yes or no.
Proof − At first, we will assume that such a Turing machine exists to solve
this problem and then we will show it is contradicting itself. We will call
this Turing machine as a Halting machine that produces a ‘yes’ or ‘no’ in a
finite amount of time. If the halting machine finishes in a finite amount of
time, the output comes as ‘yes’, otherwise as ‘no’. The following is the
block diagram of a Halting machine −

17. Now we will design an inverted halting machine (HM)’ as −
 If H returns YES, then loop forever.
 If H returns NO, then halt.
The following is the block diagram of an ‘Inverted halting machine’ −
Further, a machine (HM)2 which input itself is constructed as follows −
 If (HM)2 halts on input, loop forever.
 Else, halt.
Here, we have got a contradiction. Hence, the halting problem is undecidable.