Exactly when and why can a Turing-machine not solve the halting problem?

Question

I perfectly understand and accept the proof that a Turing-machine cannot solve the halting problem.

Indeed, this is not one of those questions that challenges the proof or result.

However, I feel that there is still something left to be explained ... I am still left wondering exactly why the Halting problem is not solvable. Of course, in the sense that there is a proof, there is a why here ... and yet ... I feel that some important other part of the why is missing.

Let me explain:

First, let's assume we just try to solve the 'empty-tape halting problem' and let's assume that the machines we are interested in only have two symbols: 1 and 0. Now, given some machine, will it halt, when stated on the empty tape (meaning: all 0) or not?

Now, we know that this problem is not Turing-solvable. If it were, we get a logical contradiction. OK, I get it. I have no problem with that at all, and like I said, I can follow the proof and I completely agree with it. I perfectly accept that this halting problem is not solvable.

But suppose I would actually try and give it a go: suppose I would try and solve this halting problem. We know the set of all turing-machines is enumerable, so let's just go through them one by one. Now, presumably this enumeration is such that it starts with relatively 'simple' machines. Indeed, I could first list all the ones with 1 internal state, then all the ones with 2, etc. since for any $n$, and with only $2$ symbols, there are only finitely many possible machines

Now, for all the machines with $1$ state, I can easily predict their behavior. Some halt. Some don't. OK, moving on to the machines with $2$ states. With some effort, I can predict the behavior for all of them as well. Cool. On to $3$ ... ok, now it becomes more difficult .. but even here I can do it. I know, because people working on the Busy Beaver problem have figured this out. And I believe they figured it out for $n=4$ as well ...

Interestingly, these researchers are using computers to help them figure out the halting or non-halting behavior for these relatively 'simple' machines. These computer programs are, in a way, trying to solve the halting problem, at least for very small values of $n$. Presumably, these machines 'analyze' and 'break down' the behavior of a machine with $4$ states into something that can be demonstrated to halt or not halt. But of course, we know they can't solve it for all $n$ ... they can't be perfect. And indeed, for $n=5$ the behavior of Turing-machines gets so complicated that human nor machine is able to figure out (yet) whether the machine halts or not.

So ... here is my question: what is it that we're running into that prevents us from figuring out the halting behavior?

The proof of the Halting problem uses self-reference. That is, if a machine could solve the halting, then we can show that thee must be a machine that halts on its own input (i.e. when given its own program, or its own number in some enumeration, or..) if and only if it does not .. a contradiction.

OK, but this is when we have a machine with certain powers ... in a way, a machine that can solve the halting problem is a machine with 'too much' power, which leads to a contradiction.

But, the halting-detection machines used by the Busy Beaver researchers don't have too much power. They have too little power. Currently they can't solve $n=5$. OK, so we give them some more power. Maybe at some point they can solve $n=5$ ... but they still can't solve $n=6$. Maybe we can give them enough power to solve $n=6$, or $n=7$ or ....

... so my question is: is there some 'special' value of $n$, say $n=m$ where this has to stop. Where, somehow, the only way to solve $n=m$, is by a machine that has 'too much' power? But why would that be? Is it because of some kind of self-reference? Because the only way to solve $n=m$ is by a machine that, as it tries to analyze and predict the behavior of some machine with $m$ states, cannot break it down to anything 'smaller' than something that requires solving $n=m$ itself? Some kind of 'minimal' value not unlike some set of minimum requirements that formal systems need to have in order to apply the Godel construction to them?

One thought I have is that this cannot be: like I said, for any $n$, there are only finitely many machines to consider. As such, it is computable; there is some machine that correctly classifies all machines with $n$ states as empty-tape halters or non-halters: it takes a machine on the input, goes through its finite list with pre-stored answers, and outputs that answer. There is a machine that does this for $n=5$, there is one for $n=6$, etc. And, none of those machines have too much power: no contradictions here. They all have too little.

Then again, these machine don't do any explicit analysis of the machines involved ... they just happen to give the right value. So, maybe there is still some value of $n$ where the approach of actually trying to analyze and predict the behavior of machine starts to break down for some fundamental, again possibly self-referential, reason?

Or: is it that the analytical approach merely gets harder and harder ... but that there is no 'special' point where it becomes, for some theoretical, fundamental reason, too hard? As such, the contradiction only comes from a machine that can do it for all infinitely many values of $n$? Indeed, maybe the problem is that in order to analyze the behavior of all machines with $n$ states, we need a machine that has to have more than $n$ states ... and so while for every $n$, there is a machine $M$ that can perform the analysis, the complexity of $M$ is greater than any of the machines with $n$ states, and hence you'd need another, even more complicated machine $M'$ in order to analyze machines with the kind of complexity that $M$ has ... thus setting up an infinite regress that you can never complete, i.e. there is no one machine that can 'do it all'?

Can someone help me how to think about this?

Answer 1

I think the following portion from your question is most important:

But, the halting-detection machines used by the Busy Beaver researchers don't have too much power. They have too little power. Currently they can't solve $n=5$. OK, so we give them some more power. Maybe at some point they can solve $n=5$ ... but they still can't solve $n=6$. Maybe we can give them enough power to solve $n=6$, or $n=7$

or ....

... so my question is: is there some 'special' value of $n$, say $n=m$ where this has to stop. Where, somehow, the only way to solve $n=m$, is by a machine that has 'too much' power? But why would that be?

The solution to solving $\Sigma(5)$ isn't simply giving Turing machines "more power." The reason we don't know $\Sigma(5)$ right now is because there are 5-state Turing machines which mathematicians believe will never halt, but have not been able to prove will never halt. The problem is not as simple as just enumerating through all of the 5-state Turing machines since once you've done that, you still need to figure out if the Turing machine halts or not, which, as you know, is not a trivial problem. We've been able to do this for 4-state Turing machines, but we don't know yet if we can do this for 5-state Turing machines because there may very well be 5-state Turing machines which we can never prove to be halting/non-halting within the system of classical mathematics (that is, ZFC).

Now, you've asked what is the magic number is: What is the magic number $n=m$ such that we will never be able to solve for $\Sigma(n)$? As I've said above, that magic number could very well be $n=5$, but that hasn't been proven yet. However, mathematicians have proven that $\Sigma(1919)$ is indeed un-knowable within ZFC. Before explaining this, I think it might be helpful to quote your question again:

Then again, these machine don't do any explicit analysis of the machines involved ... they just happen to give the right value. So, maybe there is still some value of $n$ where the approach of actually trying to analyze and predict the behavior of machine starts to break down for some fundamental, again possibly self-referential, reason?

My answer to this question is yes, there is a 1919-state Turing machine such that trying to predict if the machine halts would be fundamentally unsolvable within our system of mathematics. See, the way mathematicians proved $\Sigma(1919)$ is unsolvable is by constructing a 1919-state Turing machine $M$ which halts if there is a contradiction within ZFC and never halts if ZFC is consistent. However, there's no way to prove ZFC is consistent using the axioms of ZFC because of Godel's Second Incompleteness Theorems. This means we can never use the ZFC axioms of mathematics to prove machine $M$ ever halts or not because doing so would constitute a proof that ZFC is consistent. Thus, mathematicians can't predict if machine $M$ halts or not because of Godel's Incompleteness Theorem, which means that the busy-beaver problem for 1919-state Turing machines is unsolvable.

I hope this gives you some more insight into why $\Sigma(n)$ is solvable for small values of $n$ but unsolvable for larger values of $n$. Anyway, I am certainly not an expert in theory of computation, so if someone would like to add an alternate explanation/clarifying comments to my answer, feel free. Thanks!

Answer 2

Since, as you observe, any finite amount of the halting problem - that is, any set of the form $H\upharpoonright s:=\{x<s:\Phi_x(x)\downarrow\}$ - is computable, there isn't any particular sharp impossibility point. There are some interesting "phase transitions" which appear relevant - e.g. at a certain point we hit our first universal machine - but I don't know of any which have any claim to be the point where the halting problem becomes noncomputable.

On the other hand, as you also observe the way in which the $H\upharpoonright s$s are computable is non-uniform (otherwise, the whole halting problem would be computable). So we can try to measure this "ongoing complexity." There are two, to my mind, natural approaches:

• Given $n$, how up up the "hierarchy of theories" - from fragments of PA, to fragments of $Z_2$, to fragments of ZFC, to ZFC + large cardinals - do we have to go to get a theory which can decide whether each of the first $n$ Turing machines halts on input $0$?

• Given $n$, how complicated is the finite string consisting of the first $n$ bits of the characteristic function of the halting problem (call this string "$\eta_n$")?

Of these two approaches, the first has some draw which the second lacks, but it's also far more vague and limited. The second winds up leading to a very rich theory, namely the theory of Kolmogorov complexity (and its attendant notions, like algorithmic randomness), and also partially subsumes the former question. So I think that's my answer to your question: ultimately you won't find a sharp transition point, but the study of the dynamic behavior of the complexity of the halting problem is quite rewarding.

Answer 3

For example, you can construct a Turing machine (I don't know how many states you need, but it is a finite number) that searches for a counterexample to Goldbach's conjecture, i.e. an even number $> 2$ that is not the sum of two primes, going through the even numbers one by one; for even number $n > 2$ it checks each $k$ from $2$ to $n/2$; if $k$ is prime and $n-k$ is prime it goes to the next $n$, but if it gets through all the $k$ it halts. Thus this Turing machine will halt if and only if Goldbach's conjecture is false. In order to decide whether it will halt, your analysis will need to decide Goldbach's conjecture.

And when you're done with that one, there are lots of other conjectures you could check with a Turing machine.

Answer 4

A potential Busy Beaver has three possibilities:

1. It is easy to show it stops
2. It is easy to show it never stops
3. It is difficult to show either case

Number 1 either stops quickly, or it has a repeating pattern with an eventual flaw that causes it to stop.

Number 2 has a repeating pattern and never has a flaw, causing it to continue forever.

Number 3 is the interesting case. Its behaviour is chaotic. It might have short-term patterns, but it has no long-term patterns. Its future states can be predicted a short way without actually running the machine. There comes a certain point where its behaviour can no longer be predicted without running it. At this point you need a machine capable of emulating a Turning machine but faster. However, you will reach the same problem with this hypothetical new machine too, as long as it has finite power, which all real-world machines have.

If you improve your analysis of Busy Beavers, you can decide whether certain candidates are actually case 1 or case 2. We can think of it as a spectrum of behaviour with stopping at 0, running forever at 2, and undecidability at 1. To start with we know that 0 to 0.5 will stop (case 1) and 1.5 to 2 will run forever (case 2), with 0.5 to 1.5 being undecidable without running it (case 3). We improve our understanding of Busy Beavers. Now we know that 0 to 0.95 will stop and 1.05 to 2 will run forever, with 0.95 to 1.05 being undecidable.

At some point, there's no way to predict without running the machine whether it will halt or not. The only way to determine its behaviour is to run it, and if it stops, it usually gives you no insight you can use for other potential Busy Beavers. If it doesn't stop after $10^6$ cycles, you can try $10^7$, then $10^8$, and so on. At some point you give up without knowing whether it will stop if given enough time.

The problem is similar to drawing a Mandelbrot set. Certain points "escape" to infinity quickly, others settle into a repeating pattern quickly. The points on the boundary of the Mandelbrot set are difficult to predict either way without calculating them. There are methods to make the decision easier, but there will always be chaotic behaviour between the two easily detectable outcomes that can't be predicted.

Hopefully I've answered "why". Then "when" will be determined by your understanding of the specific problem you're trying to solve and the power of the machine you're using. Over time we can eat into the chaos and make it decidable, but it grows much faster than we can ever solve.

Answer 5

I would like to offer an alternative way of thinking about the halting problem, which helped me understanding better why the halting problem is non computable, or better, why in general there are functions which are non computable.

In his famous paper on Computability, Turing mentions that he is going to prove that there are real numbers which are not computable. Computable numbers are defined as those whose decimals are calculable by finite means, or in other words, decimals that can be computed by a machine.

He also mentions that it is equally easy to define and investigate computable functions instead of computable numbers and that is what I would like to show. I will report in short the lecture of the link I have posted already (https://www.youtube.com/watch?v=9JeIG_CsgvI&index=14&list=FLIknGRIW8gX2yutAMeHVEKw) because I think it is worth it: it is indeed the first part of a lecture which proves Goedel first Incompleteness Theorem. Credits go to "UC Davis Academics" of course.

Let's define a function $f$ from non negative integers to the set $\{0,1\}$. We let $Q$ be the set of all such functions. It is clear that $Q$ is infinite (we wil prove indeed it is uncountable infinite essentially).

Also a function $f$ in $Q$ is defined as computable if there is a computer program $P$, (loosely speaking a Turing Machine), that can take any non negative integer $x$ and output $f(x)$. We add the constraints that $P$ must always terminate in finite time and $P$ must be correct, in other words, output the correct value of $f$ for all non negative integers.

We let $A$ be all the functions in $Q$ which are computable. We can show that there exists a function in $Q$ that is not in $A$, i.e. there exist uncomputable functions.

We define a program as a series of finite statements over some finite alphabet $\alpha$, in other words it can be thought as a single finite string. Suppose that the language $L$ we are using to express our program is Turing complete, i.e. it can be used to simulate any Turing machine.

We can start enumerating in order of length the strings expressible in $\alpha$. Strings of the same length are taken based on an alphabetical order that can be defined arbitrarily in $\alpha$.

You could indeed write a program $T$ to enumerate all those strings expressible in $\alpha$, so for any string $s$ expressible in $\alpha$, $T$ in finite time will generate $s$.

Therefore you have a list $Z$ of strings in $\alpha$ ordered by length. Some of those strings in $Z$ will be legitimate programs in our chosen programming language $L$. Indeed all possible programs will be in $Z$, and in particular the programs that compute functions in $A$ must all be there (by definition of computable function) and they are ordered in $Z$.

Let's call $H$ this ordered list of functions in $A$, $\{f_{1}, f_{2},...\}$ . Now applying the diagonalization method, by defining $$g(x)=1-f_{i}(i)$$ It is easy to see that $g$ is in $Q$, however $g$ is not computable since it is not in $A$, and so we are done.