Sunday, 15 November 2015

Change layout dynamically in Android applications. A case study.

Android applications are usually built to function in a variety of devices that have different screen dimensions and also they must be functional in any orientation of the screen and in any change of the orientation. Moreover the efficient use of space especially in devices with small screens is very important and gives great value to the application, so the GUI of an activity must be designed with special care to screen sizes and orientation changes.
The indicative way to design the GUI of an activity is to prepare a layout file for it. This is a more straight forward procedure than defining the graphical elements of your app programmatically and is easier to handle and test. All the default layout files are stored in /res/layout directory of the projects file structure. In case you want to prepare a layout file for an activity with landscape orientation, you have to create directory /res/layout-land and you place the layout file in there. When the size of the screen matters and you want different UI for different screen sizes then you must create a layout for each case. For instance, a layout designed for screens with smaller size larger than 600 dp must be placed in a directory named layout-sw600dp and if you want to combine screen sizes and orientation you must place the appropriate layout file in the directory named layout-600dp-land.
All the layouts that refer to the same activity must have the same name regardless of the directory they are stored. When an app is executed, Android will search for the most appropriate layout considering the screen size and orientation. In case there is not a special layout defined, Android will use the layout stored in the default directory.
Problems start when some of the elements of the screen change dynamically while the app is running and a layout has already been loaded. In most cases the change in the orientation of the screen causes troubles in the dynamically changed elements. When orientation changes, Android usually reloads the activity losing all the changes the user has caused. In order to avoid that you must edit the manifest file and in the activities that are declare some how like this

            android:label="@string/title_activity_myactivity" >

add one line of code changing them like this

            android:label="@string/title_activity_myactivity" >

This tells Android that you know what to do with your activity so it shouldn’t change anything on its own. Let’s see now what you should do when it is you that you want to cause layout changes when orientation changes. In order to present this more efficiently I will use the example of my app called Map This Way.
In an activity of this app called activity_line the user may record the GPS tracks along a route. In the layout there is some text that is updated with the current GPS coordinates and a map view that shows user’s current location. In big screens when the screen is in landscape orientation the map is better to be on the right of the screen and the text on the left and when the screen orientation is vertical the map view is better displayed under the text. In small screens the map view should always be under the text. In order to do this I prepared layout files for all cases. Then I had to arrange how the app will behave in screen orientation changes.

Orientation changes are detected and manipulated by the onConfigurationChanged method which must be overridden.

public void onConfigurationChanged(Configuration newConfig){

The above code reloads the UI of the activity using the most appropriate layout file for the new orientation of the screen. The advantage of using this technique instead of letting Android do the changes is that in this method you may add code to define how the activity elements will be displayed in the new screen. The disadvantage is that the elements are initialized and reloaded so you will have to add code so as to initialize them properly and update the values of the layout elements with the changes that have occurred so far. Also you have to restart procedures that may have stopped after orientation changed like advertisement display etc.
In the case of my app the map view has to be updated so the users will see their current location and if the mapping procedure has started the colors and functionality of layout elements has to be adjusted accordingly.
In general, a good way to deal with such situations is to initialize the layout elements in the onConfigurationChanged method as you did in the onCreate method. So for update of the map view of my app I use the method showmap(x, y) which updates the location of the map and it is executed in both methods.
Another problem occurs when you have to pass some value from a variable that affects layout elements to the newly initialized elements after orientation changes. For this, the use of some global variable is very helpful. In my app when the mapping procedure has started, a red button appears which may be pressed by the user and cause the stopping of the mapping procedure. The way I worked around this issue is the declaration of a global boolean variable called mapping which takes the value true when mapping has started and false otherwise also the declaration of a global Button instance. Then I used both in onConfigurationChanged method.
All the above were formed as follows.

public void onConfigurationChanged(Configuration newConfig){
   showmap(x, y);
   if (mapping){
        button1=(Button) findViewById(;

Then activity_line is functional and ready to use.

Friday, 9 October 2015

Against the idea of a computable universe

The computable universe hypothesis, states that every operation and phenomenon in the universe is computable. So if you can describe all the phenomena in state s1 of some part of the universe (or of the whole universe), then you can compute the physical operations in any state si that is a successor of s1.
An equivalent hypothesis is that every set of physical operations can be simulated by a Turing machine or an automaton. This means that there is a Turing or some other computational machine that will accept as input the data that describe these operations and will output the result of them and then it will halt. The two hypotheses are equivalent as the simulation of every operation by a Turing machine implies the computability of the transition from state s1 to any state si.
These hypotheses also support the idea that the universe is deterministic. The transition from s1 to si is computable and as a result predictable which makes it deterministic.
There are many papers and textbooks that propose theories that are based on these hypotheses, there are also many that oppose such ideas. My opinion is against the idea of a computable universe, the reasons for this are: randomness, incompleteness and uncomputability.


The existence of randomness in physical processes remains a debate among scientists. Einstein rejected it, as he believed that universe is deterministic and stating that "God does not play dishes"; a phrase that marked the debate. The truth is that there are many indications that some physical operations must be random.
Steven Hawking provides some very convincing arguments in one of his lectures. According to Hawking the way the universe evolves seems to be not deterministic, moreover the existence of extreme objects in it like the black holes and the Uncertainty Principle makes accurate computations and predictions impossible. Although in computer science we can only use pseudo-random operations in order to approach true randomness, in other fields true randomness seems to be present. Quantum mechanics build probabilistic models in order to study quantum phenomena that are believed to evolve truly randomly.


Any not trivial system of logic is incomplete. This is a very simplified description of the incompleteness theorem stated and proven by Kurt Godel which is one the most important theorems in science.
Any system physical or not that operates under certain laws may be efficiently described by mathematics and more specifically by some system of mathematical logic using propositions and axioms. If the system of logic is not trivial (is not very simple) then there are true statements in it that may not be proven as true; the system is incomplete. This results that there are phenomena that may not be computed by mathematics.
This lecture of Hawking approaches incompleteness theorem from a physicist perspective reaching the same conclusions.
The issue that we face is that in order to compute some mathematical object O we need to use or to build some proper mathematical structure. But no matter how this structure will be it will always be incomplete, so a part of O will not be able to be proven using our mathematics.


The theoretical background of uncomputability in Computer Science is related to Godel’s incompleteness theorem and states that there are instances of computational problems that may not be computed by a machine or a human. Computational procedures may be simulated by Turing Machines that perform them and then halt, so we usually state that a problem P is uncomputable if there is not a Turing Machine that may compute all the instances of P and then halt. Equivalently, if you input some data in a Turing Machine you may not know if it will halt or not.
The foundation for this was set by Alan Turing before the first general purpose computer was ever built.
Its affect in programming is something that we experience every day. No matter how hard have you worked on a piece of software you can never be sure that it will work as expected.
A parallelism in physics that I have thought is included in the question 
“How can you be sure that for operation H there is a Turing Machine that will halt on input of the description of H?”
The above arguments may seem to be weak but there is a stronger one for which I talked about in a previous post.
Causation (or else causality) refers to the cause-effect relation that may exist between two events or phenomena. The causal inference problem regards the determination of causation between events.
In my previous post I present a proof on which Causal Inference is logically and computationally undecidable. Relations among phenomena and physical processes are important factors in the structure of the universe. The Causal Inference undecidability results that there are physical phenomena for which we may not decide or compute if they are related to each other or not. This makes a part of the universe uncomputable and unexplained using logical arguments.

Thursday, 17 September 2015

How to make a hosted web app listed in app stores and installable for the end users

Building a hosted web app has many advantages. One advantage is that it is not necessary to create big software packages containing the whole app where the users will have to download and install. It is only necessary for them to open the main page of the app and work with it. Also the app may be updated whenever it is necessary and the end users receive the update in real time.
Hosted apps do not require to be placed in an app store and follow certain specifications on the way they are built. Just find a host which may be free, paid or the server of your company and put your creation in there.
There is a small down side in this practice. Apps that require installation create a stronger bind to the end users while hosted apps have a more loose attachment to their users. Also, apps that are listed in an app store receive some promotion just by being there; they may be searched and found easier.
The good news are that there are three app stores that list hosted web apps and two of them provide some methods of installing a link or a small app pointing to your hosted app in the end user's computer.
I had to discover all these things I write about in this post after I finished developing WMS Map Viewer on line. It is a web app and it was really easier to develop it than to find out what to do with it afterwards.
An easy choice for listing your app is the Opera Mobile Store. The store lists apps mainly for mobile devices (new, old and very old) but also accepts submissions for HTML5 web apps for desktop computers. The submission procedure is similar to any other app store. The submitted apps are being reviewed before publication (which I find a good idea) and usually it doesn’t take more than one or two days. When you click on a web app you view its store listing and there is a download button which simply opens the homepage of your app. See my listing for example.
The other two app stores are the Chrome Web Store and the Firefox Marketplace. Both stores address to certain browsers and they both share an interesting feature. When a user downloads an app from these stores the app (or at least a link) gets installed on the user’s computer.
In Chrome Web Store you have to pay a onetime fee (like in Play Store) this fee is currently 5$ and you may place up to 20 apps. Then you have to confirm that the web page you submit is yours by logging in to Google web masters and put a metadata file in the root directory of your app. You can do the submission procedure with any browser but the apps are available for download only in Chrome.
During submission it is only necessary for you to provide a .json type manifest file, a description of the app and some screenshots. But as the store accepts submissions of apps, themes and extensions for Chrome with the same procedure, you have to be very careful and submit the correct manifest. Otherwise the submission procedure might assume that you submit an extension and then your store listing will simply not work. The only way to solve this if it happens to you, is to make a new submission. The previous one is characterized as an extension and this cannot be changed and the submission cannot be deleted. Use this tutorial for instructions don’t forget the part

  "app": {
    "urls": [
    "launch": {
      "web_url": ""

this makes all the difference between an app and an extension.
See the listing of my app as an example.
After listing your app, any Chrome user may download it. Chrome places downloaded apps in the app menu regardless if they are packed or hosted. So the apps are functional in the Chrome environment like any other app or extension.
In Firefox Marketplace you are also required to place a .json type manifest, but as it has to have the .webapp extension you have to declare in the metadata of your server that the manifest is of type
this is a platform dependent procedure and there is a lot of documentation about it in the help files of the Marketplace. The syntax of the manifest is quite trivial, unless you wish to put special installation parameters.
The interesting thing is that when a Firefox user downloads an app in Windows, the app is installed like a regular windows app that may be removed only from the Control Panel. In fact, Firefox installs a small app which is basically a browsers window named after the downloaded app with some cache memory. When a user runs this installation the hosted app opens in this window which has different functionality than Firefox normal windows. This feature is useful as your hosted app runs like a local app improving user experience.
The downside of Firefox Marketplace is the review process. It takes a lot of days, a week or more. And some of the reviewers must not be very experienced. The reviewer of my app didn’t know how to run it; despite the help files in it. So I had to send him detailed instructions and wait even more days for the approval of my submission.

Tuesday, 25 August 2015

An online map viewing application

WMS Map Viewer on line is a light-weighted client for web map services that use the WMS protocol. It is a web application compatible with all modern versions of WMS and also supports the projection of kml files as overlays in the chosen WMS connections.
It is a follow up of my Android application and I like especially the fact that you can simply open it in a browser and just work with it. Most of the WMS viewers are heavy applications that require to be downloaded installed etc.
Here are a few of the many sites that you  can find and provide WMS maps.

CUZK Geoportal - Orthophoto


Sunday, 19 July 2015

Proving uncountability without diagonalization

In this post, I propose a proof of the uncountability of the set of real numbers without using the famous diagonalization method of Georg Cantor. My proof is based on the Church-Turing thesis which states that any computable operation may be computed or simulated by a Turing Machine. So, if a set of mathematical objects is countable a counting operation may be performed and there is a Turing Machine that may compute it.

Definition. Let S be an ordered set of discrete objects. If S is countable then there is Turing Machine M which on input of the descriptions of objects a and b it outputs c which is a representation of the number of elements in S between objects a and b.

If we may count the elements between a and b (or else the distance of a and b in S) then we may count the whole set, as a and b may be any objects. This definition avoids a direct connection of the set N of natural numbers to S, so it differs from diagonalization, yet as I show below it reaches the same conclusions on natural, rational and real numbers. Output c has to be a number and not the symbol of infinity or anything similar, otherwise we cannot consider it as a counting result. First let’s test this definition on natural and rational numbers.

Theorem 1. Set N of natural numbers and set Q or rational numbers are countable.

Proof for N. Set N may be defined as an ordered set of linear structure. For every i we place in the i-th place of N number i. Let a and b be the representations of two natural numbers where a represents a smaller value than b and M a TM that computes their distance. In order to make the presentation more simple and intuitive and without loss of generality, let M be a 2-tape machine and a, b and c be represented in unary numeral system. Then we print a in tape-1 and b in tape-2. For any “1” printed in tape-1 M erases one “1” that is printed in tape-2. Finally a representation of c is left in tape-2 and this is the output.

For instance, on a = 8 and b = 13 we print
tape-1 : #11111111######
tape-2 : #1111111111111#
After running the configuration of M we get
tape-1 : #11111111######
tape-2 : #11111#########

Proof for Q. Let’s first define Q as an ordered set. It is known that Q may be defined as the Cartesian product of sets X and Y, where X and Y are equal to N / {0}. For every ordered pair (x, y) of the product there is corresponding number q which is in Q and stands that q = y / x. Using this definition we may create an ordered set of linear structure equivalent to Q.
Set Q may be structured as an ordered set of subsets. Initially we place numbers “0” and “1 /1”, then for every i > 2 we place in the i-th place of the set the subset of Q that for all its elements stands x+y=i. By placing in ascending order the elements within each subset we create an ordered set of linear structure with all the elements of Q. On this procedure we end up with a representation of Q that is commonly used in literature.
On input of the descriptions of a and b, TM M using the above procedure creates the ordered subset of Q that contains a and b and then computes their distance by simply counting the elements between them one by one.
The ordered set of linear structure that is subset of Q and contains a and b is computable as it may be constructed algorithmically as shown above and is finite as I will show next.
Let a=y1/x1 and b=y2/x2, then between a and b there is a finite number of subsets d= (x2+y2)-(x1+y1). For each subset stands that i=x+y and is finite as there is a finite number of combinations that may generate i given that i, x, y belong in N.

Cartesian product

1/1   1/2   1/3   1/4 ……
2/1   2/2   2/3   2/4 ……
3/1   3/2   3/3   3/4 ……
4/1   4/2   4/3   4/4 ……

Order of elements

 1       2       4       7  ……
 3       5       8      11 ……
 6       9      12     14 ……
10     13     15     16 ……

Q as set of subsets

(0), (1/1), (1/2, 2/1), (1/3, 2/2, 3/1), (1/4, 2/3, 3/2, 4/1), (… 2/4, 3/3, 4/2 …), (… 3/4, 4/3 …), (… 4/4 …)

Q as an ordered set of linear structure

0, 1/1, 1/2, 2/1, 1/3, 2/2, 3/1, 1/4, 2/3, 3/2, 4/1, …, 2/4, 3/3, 4/2, …, 3/4, 4/3, …, 4/4 …

Theorem 2. The set R of real numbers is uncountable.

Proof. It is known that there is no algorithmic procedure which may produce the numbers in R. That is because most of numbers in R are uncomputable. 
Let's consider a description d for the numbers in R so that they may be represented in TM M. If there is procedure implemented in M that structures R as an ordered set then M computes uncomputable numbers which is a contradiction. As a result R may not be structured as an ordered set and is not countable by M.

Friday, 26 June 2015

System security as a computational problem

In this post I talk about system security and I examine it as a computational problem. The motivation of thinking about this came from a very interesting post by Dick Lipton and Ken Regan that was published on their blog a few days ago.
The problem of developing a secure system regards the development of a system that will maintain a set of information F which will only be accessible by authorised users. As a result, any unauthorised user will be excluded from accessing F. A good way to study the system is to model it using Turing thesis.
Any user  has to provide some input like username and password in order to get clearance and usually F is represented by some text or binary information, so let's define F as a set of strings and w as a string which is the user input. Also let A be a Turing Machine that simulates the system we have developed and we consider it secure.
Some systems in order to identify authorised users demand extensive interaction which results that users have to fill questionnaires and forms. Without loss of generality, we may enclose all the user input in w also the acceptance or rejection of it by the system may be modeled efficiently by the operation of A. On this assumption, A will accept a valid w and output F or reject input from not authorized users and output "NO".
Let S be a set of strings so that if A decides that input w belongs to S then the user is considered as authorized and F is outputted. There are two cases where this model does not work as expected.
Case1. There is string e that does not belong to S and on input in A, it outputs F. So an intruder may use e in order to gain access to F without knowing w.
Case2. String w may be discovered algorithmically by unauthorized users.

Some explanation on these cases.
In case1 I model the cases of the real world where A is hacked. The intruder does not input the identification of some authorized user in order to access F, so user input does not belong to S. The intruder inputs a string that will cause A to get undesired behavior and this will result the output of F. In real world, sometimes the intruders exploit security holes and send data to the systems (like macros, telnet commands or Trojans) that cause such undesired behavior.

Case2 refers to the complexity of w and how easily it can be guessed.

Intentionally I leave out the cases where w has been stolen from its owner with techniques likes phishing or similar; this seems to be more of a social than a computational problem.

The computability of security and why hacking is easier that defending

The following language describes Case1 as a computational problem.
SECURITY1 = {A, S, F, e | There is string e that does not belong to set S, TM A outputs F and halts on input e.}

Theorem. Problem SECURITY1 is recognizable and undecidable.

Proof. SECURITY1 is recognizable. We input in A string e that does not belong to S and we get F. If SECURITY1 is also decidable then for any input s that does not belong to S, we may decide whether machine A halts and outputs F or “NO”. As A can be any TM and s any string, these assumptions stand only if the halting problem is decidable and this results a contradiction.

Although both the developer of A and an intruder face the same problem, as they both have to solve one instance of SECURITY1 in order to build or hack A. Yet, the job of the intruder is easier than the job of the developer. The intruder has to solve the recognizable side of the problem while the defender has to approach the undecidable side of it.
In other words, if you try to intrude A you build a process described by string e and once you input it to A you can find out if it works. If you are a defender you can never be sure that the system that you build will be strong enough to resist attacks. Building A is a far more complex task than discovering e and that is why you see systems that are developed by experienced developing teams to have been hacked by some technology enthusiast teenager.


About Case 2

Case2 refers to the complexity of w or else to the complexity of the procedure of entering user identification data. There is no point on examining the computability of such processes as most of them are in theory computable. For instance, when a user has to enter a 4 digit PIN in a system the number of possible PINs that may be entered is bounded and in theory it is computable. If we enter the restriction that the user has only three attempts to enter the correct PIN then it gets difficult for an intruder to guess the PIN.
The proposed approach by Lipton and Regan actually increases the complexity of the procedure of identification. It gets harder for an intruder to guess w which may be subject to frequent changes if the questions asked by the system change frequently.

The image on this post is freely available under Creative Commons License. Check the source for the original image and many more.

Wednesday, 27 May 2015

Building interaction between Android and Javascript

In this post I present methods on how to build interaction between Javascript content in a webpage and the Java code of an Android application. When building an application with web content (build-in or loaded from network) there is always the issue on how it will communicate with the rest of the application. The interaction has to be two-way. The web content probably hosted in a webview has to be able to affect the rest of the application based on user interaction and Android must be able to change the web content. There is not a unified way to build a two-way bridge between the two interfaces, Javascript and Android have to bind with each other using separate processes.
Let’s suppose that you are building an app with one activity named Activity1that hosts a webview which opens the local file HTML1.html (the examples that follow also work when files are somewhere in a network and not local). In Activity1 there are some String variables v1, v2, v3. Corresponding variables with capital letter names are defined in some javascript code enclosed in HTML1.

At first the variables and the webview in Activity1 have to be declared properly and Javascript support has to be enabled.

Code to place in Activity1

String v1, v2, v3;
WebView webView;

//In the onCreate method add
webView = (WebView) findViewById(;
WebSettings webSettings = webView.getSettings();

Variable declaration in a script in HTML1.html

var V1=”Value of v1”;
var V2=”Value of v2”;
var V3=”Value of v3”;

Updating variables in Javascript

I present two techniques for updating the variables in Javascript. In the first the necessary code will be loaded in Javascript before HTML1 will be loaded in webview and this is the main advantage of this technique.
In order to change the Javascript code that runs in webview, I create programmatically a file that contains the code I care to run and then I insert it to HTML1. As a technique may not be so elegant, but it has certain advantages. Another advantage of this technique is that the preservation of the information that regards the variables does not depend on the life circle of Activity1. So, the file may be store while running Activity1 and other activities that will open in the future may read it and update their variables.

In Activity1

At first the path of the file that will be dynamically created has to be defined. A good choice is to place it in the files directory of the application. The directory path can be accessed using the getFilesDir() method. Then it is only necessary to add the code that creates the file.

File jsfile = new File(getFilesDir() + File.separator+"new.js");

public void filecreator(){

v1="new value1";
v2="new value1";
v3="new value1";

BufferedWriter buffer = null;       
        buffer=new BufferedWriter(new FileWriter(jsfile));
    catch (IOException ex){
        String ioerror=ex.toString();

Then simply call the method while running Activity1.

In HTML1.html

Insert the newly created file in the body or the head of the html code.

<script src="jsfile.js" type="text/javascript"></script>

If both HTML1.html and jsfile.js are in the same directory then it is not necessary to determine the full path of the file and the code above is enough.

Another more elegant technique is to load the script using method
webView.loadUrl(("javascript: /*some code*/")
In this case you only add the following code in Activity1 and don’t make any changes in HTML1.

webView01.loadUrl("javascript: V1=’new value1’; V2=’new value1’; V3=’new value1’");

The above script will be executed after HTML1 has been fully loaded in webview. You have to be careful on this, as executing the script before loading the html file might lead to undesired behavior.

Updating Android variables from Javascript

Let’s see how running Javascript can affect the variables of Activity1 by modifying the example in Android Developers site (link).

In Activity1 

Add a WebAppInterface class. The methods of the class must overidde JavascriptInterface in order to work with the latest versions of Android.

public class WebAppInterface {
   Context mContext;

   WebAppInterface(Context c) {
       mContext = c;
   public void updatevariables(String V1, String V2, String V3) {

In this implementation, the method updatevariables is called from Javascript that runs in the webview. The values that should be set in the variables v1, v2 , v3 are passed to the formal parameters of the method (String V1, String V2, String V3) and the code that follows updates the variables in Activity1.

Code to place in HTML1.html

function updatevariables (V1, V2, V3) {
    Android. updatevariables (V1, V2, V3);

In some part of the script in HTML1.html call the function updatevariables and the values of v1, v2, v3 will be updated.

John Forbes Nash (1928 – 2015)

Alicia Nash (1933 – 2015)



Friday, 3 April 2015

On the computability of causal inference

Causation (or else causality) refers to the cause-effect relation that may exist between two events or phenomena. The causal inference problem regards the determination of causation between events.
Let’s consider the case where there is an interaction between two events which is not affected by any other factor or event. The two events are observable and measurable and for each one we may record a set of data that describes it. Then the causal inference problem is equivalent to the existence of a computable relationship between the two data sets that describe the events.
As I show next, the general case of the problem of causal inference even with the above assumptions is undecidable and this has important implications in science. The undecidability of a problem means that there are some instances of it that may not be proven true or false, while other instances may be decidable.

Let’s formulate the general case of the causal inference problem as a decision problem. Let A and B be two sets of data.

CAUSAL = {A, B | There is computable function f(A) = B}
It is enough to use the formulation of CAUSAL in order to describe causal inference problem. If there is a cause-effect relationship between A and B then there is function f so that f(A) = B and if CAUSAL was proven decidable then f would be computable in any case.
Let me restrict this discussion in data sets and phenomena that their measurements are as accurate as it is demanded. The meaning of this assumption is that the data of the problem is not affected by factors that would make their computation inaccurate like the principle of uncertainty. Also, this implies that the elements of both sets are computable. The earlier assumption that the two events interact and are not affected by any other factor simplifies the definition of the problem, as it implies that if there is computable function f between A and B this describes a cause-effect relationship and it is not coincidental.
In the general case the problem is not trivial as A and B may be the products of complex or even chaotic systems and natural procedures. Here the term “trivial” is used in the sense of mathematical logic.

Theorem. The causal inference problem is logically and computationally undecidable.

Proof of logical undecidability. If the problem is decidable then we may construct a system using which we may prove every instance of the problem. We may encode using Gödel numbering and arithmetic each element a and b of A and B in a formal system of logic P so that for every sentence S of the form a -> b, S or -S is provable so that eventually the sentences A -> B or -A -> B will be provable. The problem is complex so P will be sufficiently strong.
This consideration results a contradiction, as proven by Kurt Gödel (Gödel, 1931) in such a system there are true sentences that are not provable. The problem is logically undecidable.

Proof of computable undecidability. If CAUSAL is decidable, then there is Turing Machine M which on input of a string e that contains the descriptions of A and B always halts and outputs “YES” or ”NO”.
As the problem is assumed decidable and M is computable, we may construct Turing Machine N that when inputting string w that contains the description of M and e verifies that M decides e; N accepts w in any case. There are no restrictions in the nature of A and B or their encoding and representation in e, hence w can be any string. Also there are no restrictions in the description of M and N, they can be any Turing Machines.
As a result, the language ATM = {N, w | N is a Turing Machine and accepts w} is decidable. This is a contradiction. As it is known from literature that ATM is undecidable; see (Sipser 2006, p.179). Hence CAUSAL is also computationally undecidable.
Another way to reason about the computational undecidability of CAUSAL is to consider Turing Machine N that simulates the phenomenon described by A. We input in N a string w that describes set A, then N produces a string that describes set B and then halts. If CAUSAL was decidable then N would be computable. The string that describes A may be any string as there is no restriction to the event that A describes; so N halts on any given input. Also N may be any Turing Machine.
Still these assumptions lead to the same contradiction; if CAUSAL was computable so would be language ATM.

Laplace’s demon

The undecidability of causal inference has an effect on the famous idea of the demon of Laplace.

We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes.

Pierre Simon Laplace, A Philosophical Essay on Probabilities 1820
source: Wikipedia, also in (Laplace, 1951, p.4)

The intellect that described in the text was later named “Laplace’s demon”. There has been criticism against this idea and some published proofs against it. The proofs presented above are also against this idea. Such an intellect or demon should be able to decide CAUSAL which is undecidable.

The extend of undecidability

In every undecidable problem there are some instances that are decidable and some that are not. To put it another way, each undecidable problem is undecidable to some extent. Logic and the theory of computation have not been able to determine this extent for every problem. For some problems like the halting problem this extent seems large; only very simple instances of the halting problem seem computable.
In causal inference, things must be different. Science and especially Artificial Intelligence has managed to build successful methods in computing quite a lot of instances of the problem. The importance in these methods is that they may be applied in the analysis of real world phenomena. In my opinion, some of the reasons of successful predictions in real world phenomena are bounded inputs, relaxation, approximation and strong hypotheses.

Bounded inputs. Real world phenomena usually don’t take arbitrary large or small values. The possible values in the parameters of a phenomenon are bounded. This results that the problem range is reduced making computations easier.
Relaxation. Relaxing some parameters in the definition of a problem might lead to the computation of an easier problem. In some cases the conclusions from the definition of the easier problem are as valuable as the solution of the initial problem is.
Approximation. It is quite often that the solutions of hard problems may be approximated, which is valuable when precision is not very important.
Strong hypotheses. Sometimes scientists have strong evidence and strong intuition about a certain universal truth that cannot be proven or it is at least very difficult to be proved. In these cases researcher state it as a hypothesis and accept it as an unproven true fact and they may work on it overriding the obstacle of finding a solid proof for it. The biggest part of this post is based on such a hypothesis; it is the Church - Turing Thesis (Turing, 1936).


Gödel, Kurt, 1931. Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme, I. and On formally undecidable propositions of Principia Mathematica and related systems I in Solomon Feferman, ed., 1986. Kurt Gödel Collected works. Vol. I. Oxford University Press, pp 144-195.

Laplace, Pierre Simon, 1951. A Philosophical Essay on Probabilities, translated into English from the original French 6th ed. by Truscott,F.W. and Emory,F.L., Dover Publications (New York, 1951)

Sipser, Michael, 2006. Introduction to the Theory of Computation, Second Edition. Thomson Course Technology, USA.

Turing, Alan M., 1936. On Computable numbers, with an application to the entscheidungsproblem. Proceedings of the London Mathematical Society. 42, 230–265.

Thursday, 19 March 2015

Why we cannot build a general method for algorithmic error correction

This post is part of this paper.

A few years ago, I was working as GIS programmer and administrator in a company that was mainly producing digital maps for navigators. At that time, we had to map the road network in big areas and cities from scratch and also update older maps in other areas. One of the issues that you have to deal in projects of this kind is the errors that occur during the mapping procedures. When there are 10 or more people that map a road network of 20.000 road segments it is unavoidable that some (few or quit a lot) of the roads will be mapped incorrectly for various reasons. For instance, a bridge may be accidentally mapped as a normal road or some road may be not characterized as unpaved while it is.
The only way to deal with this situation is to define some specifications for the mapped road network and then build some software that will detect the cases where your map is not compliant to the specifications. You may find the description of some methods of building such software in my paper (link). Then some experienced person has to look into the detected errors and correct them.
At that time I had a big disagreement with my employer as he believed that we could build software that could correct our maps without human intervention. He thought that as we know the specifications that the network should have and we also know the cases where the specifications are violated we could also correct them algorithmically. Besides him and his opinion, I have read some papers published in conferences in which their authors support more or less the same idea while in other papers in journals it is supported that this problem may be solved only partially and with some caution.
As I will show next such a problem is in generally undecidable by computers and it may be solved only in simple instances. I always believed that this should be common knowledge.
In fact the algorithmic correction of maps is a special case of a more general problem, the algorithmic correction of software bugs. In both cases you have some specification that may be written in some formal way, say logic. Then you may detect some errors or bugs and you need to manipulate the parameters and the structure of your program or dataset so as to meet your specifications.
Moreover we may model both problems as graphs. As it is known from software technology, a piece of software may be modeled as a graph. The nodes of the graph are the functions and the other units that consist the software and the edges model the ways that the units interact. The nodes and edges may be represent really complex software components or simpler components like databases with spatial information as in road networks. Studying the more general problem we decide the simpler problem as well. So in order to study this issue you have to answer one question.
Since we can build software that detects errors can we build software that algorithmically corrects these errors?
An empirical answer to this question would be “in general, no”, it is very difficult to build an algorithmic process that would take into account all the necessary parameters and lead to the desired result by correcting all the instances of errors in a piece of software or in a map. Let’s define the problem and prove its undecidability. On the proof, I use the Turing Machine (TM) as a model of general purpose computer and define the problem of algorithmic correction of software errors as an equivalent computational problem of algorithmic correction of the input string of a Turing Machine.
Let M be a Turing Machine that accepts input w. If w is build under certain specifications M prints a message of acceptance and then halts. In case w does not comply to specifications M might not halt or have unpredicted behavior. Now let N be another Turing Machine that examines any given input w. If w is incorrect according to specifications and the description of M, then N corrects w so M can run w properly and then halt.


INPUT-CORRECTIONTM = {<M, N, w> | M and N are TM, N modifies w according to the description of M so that M halts on input of modified w}

In order to prove the undecidability of this problem it is necessary to define another undecidable problem the HALT problem. Undecidability implies that it cannot be decided by a Turing Machine and an algorithm that solves it for any given input w cannot be built.

HALTTM = {< M, w> | M is a TM and M halts on input w}

Theorem. The INPUT-CORRECTION problem is undecidable.

Proof: Turing Machine N modifies any input w, according to the description of M, so M can run w and halt. If w is correct N prints “YES”, M runs w produces an output and halts. If w is incorrect and M cannot halt while running w, N corrects w and prints “YES” then M runs w produces an output and halts.
Let H be a machine that simulates the operation of both M and N, then H halts on any given input and the HALT problem is decidable for H and any given input w. This is a contradiction since the HALT problem is undecidable.

Another approach

The approach of the proof might look difficult to understand and especially its connection with the correction of errors in spatial networks. Let’s look into it from a more practical perspective.
The specifications of every network define the valid spatial relationships of the network elements as well as the valid attributes of every element. Attributes and spatial relationships cannot be examined independently since the validation of both of them ensures network consistency. Let A be the set of all the possible valid combinations among the valid spatial relationships and the valid attributes. Let B be the set of all the possible combinations of spatial relationships and attributes that are considered invalid according to specifications.
Let f be an algorithmic function that may correct the errors in a given network. Then f accepts as input one element of B and outputs one element of A. This means that each element of B must correspond exactly to one element of A; f must be 1-1 function or many-to-one (n-1). But this can happen only to very special and simple cases, it cannot be applied in general. If an element of B corresponds to many elements of A then f may not be defined.
On the same way we may define sets A and B for any other software structure. An 1-1 or n-1 function among software with bugs and error free software may be found only in very simple instances.


A more intuitive example that concerns spatial networks.


Let N be a non-planar network of linear features. Let a and b be two elements of N where the edge of a crosses the middle of the edge of b. Suppose that the person who draw a and b neglected to apply on them the necessary parameters that denote their structure and state. We may consider the following possible cases.
  • The edge of a is on ground level and the edge of b overpasses a, edge b represents a bridge.
  • The edge of a is on ground level and the edge of b underpasses a, edge b represents a tunnel.
  • Both edges are not on ground level and edge b overpasses edge a, multilevel traffic junction.
  • Both edges are in ground level, a and b represent a crossroad. Both edges must split on the intersection point and form a proper crossroad.
There is no way how we can build an algorithm that will decide which of the four cases corresponds to the given instance, so as to correct the error.
If we consider a special case of network instance in which we may find an 1-1 function between sets A and B, we could perform algorithmic correction of a specific error case. As it is easy to see this error case will be very simple. For instance, if we consider a network where each element that participates in a specific computable spatial relationship must be characterized by an attribute of a single value. Then an algorithmic process that assigns this value to the attributes of the appropriate elements may be build. Nevertheless this would be of minor importance in the complex task of error correction.
As a conclusion, I have to say that important corrections of detected errors in maps and in general in computer software have to be manual and of course by experienced personnel.