Simple Parrot Turing Algorithm in C#
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Apr 18, 2006
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This article implements a simple chatbot to attempt to pass a Turing test, which fails miserably.
Introduction
I am extremely interested in machine learning. However, implementing neural nets and Bayesian graphs etc., can take a long time, and are subject to domain definition problems. While they are the cutting edge, sometimes it is nice to sit back, relax, and do something no rational computer scientist would do: implement a worthless algorithm.
Actually, the algorithm is not worthless. Designing the code and running through it reveals weaknesses in the approach that makes it easier to conceptualize more advanced methods and accurately define the domain.
This algorithm will break typed messages up into words, analyze the frequency of words, and then generate a random response based on the frequency of words, the minimum sentence size, and the maximum sentence size. Once implemented, it is a fun algorithm to play with (implement an IParticipant for human interaction) as it sometimes makes some interesting combinations of words.
A comment about the code:
I do not like attaching Zip files full of code unless absolutely necessary. Half of the fun of something like this is in the implementation. It also helps with the understanding. Besides, I hate having to download a Zip so I can find the answer I want. If I pose a question in this tutorial and give an answer, it is in the tutorial without the need for a download, unzip, and grep!
Background
If you are unfamiliar with a Turing test, please visit: The Turing Test. Other interesting Turing problems are CAPTCHA's which are fun to implement as well. In fact, my next tutorial may be on a captcha.
Using the code
Below is some unexplained code that should be obvious but are probably required for understanding the other aspects of this tutorial. For reasons I don't understand, I have IParticpant calling Conversation.addParticipant(IParticipant). There is probably a better design pattern implementation, I just wasn't spending time with design issues.
public struct Message{
public IParticipant speaker;
public Conversation conversation;
public DateTime timeStamp;
public string message;
/**
* <summary>
* Provides a tidy string rep of this object
* </summary>
*/
public override string ToString() {
return timeStamp.ToShortTimeString() +
" " + speaker.getName() + ": " + message;
}//end ToString
/**
* <summary />
*/
public Message(IParticipant speaker, Conversation
conversation, string message){
this.timeStamp = System.DateTime.Now;
this.speaker = speaker;
this.conversation = conversation ;
this.message = message;
}//end Message
}//end Class Message
public interface IParticipant{
string getName();
void newMessage(Message message);
void joinConversation(Conversation conversation);
}//end IParticipant
public class Conversation{
List<IParticipant> participants = new List<IParticipant>();
//Allows the message log to be saved
//List<Message> messages = new List<Message>();
/**
* <summary>
* This method will allow a message to be "spoken"
* </summary>
*/
public void speak(Message message){
//messages.Add(message);
foreach(IParticipant p in participants){
//Notify all particpants
//of a new message in this conversation
p.newMessage(message);
}//end foreach
}//end speak
/**
* <summary>
* This method will add a participant to the conversation
* </summary>
*/
public void addParticipant(IParticipant participant){
if(!participants.Contains(participant)){
participants.Add(participant);
}
}//end addParticipant
}//end Conversation
Below is the class that implements the parroting logic. This code is easy and straightforward in most cases, so I will not burden the reader with silly things like a lot of explanations.
/**
* <summary>
* This class holds word frequencies
* </summary>
*/
public class Word : IComparer<Word>{
private string word;
private int frequency = 0;
public event Action<Word> FrequencyChanged;
/**
* <summary>
* This method will return the word
* </summary>
*/
public string getWord(){
return word;
}//end getWord
/**
* <summary>
* This method will return the frequency
* </summary>
*/
public int getFrequency(){
return frequency;
}//end getFrequency
/**
* <summary>
* This method will increment the frequency
* </summary>
*/
public void incrementFrequency(){
frequency++;
onFrequencyChanged();
}//end incrementFrequency
/**
* <summary>
* This method is called when the frequency is changed
* </summary>
*/
protected void onFrequencyChanged(){
if(FrequencyChanged != null){
FrequencyChanged(this);
}//end if
}//end OnFrequencyChanged
/**
* <summary>
* Compares words by frequncy
* </summary>
*/
public int Compare(Word x, Word y) {
return x.getFrequency().CompareTo(y.getFrequency());
}//end Compare
/**
* <summary />
*/
public Word(string word){
this.frequency = 1;
this.word = word;
}//end word
}//end word
/**
* <summary>
* The bob algorithm is a trivial turing test
* </summary>
*/
public class ParrotAlgorithm : IParticipant{
private static int instanceCount = 0;
private string name =
"Parrot #" + instanceCount.ToString();
private Conversation conversation = null;
private List<Word> list = new List<Word>();
private Dictionary<string, Word> hash =
new Dictionary<string,Word>();
private int minMessageSize = 3;
private int maxMessageSize = 10;
private Random random = new Random();
private double chaos = .2d;
private int wordCount = 0;
/**
* <summary>
* This method will return the name of the speaker
* </summary>
*/
public string getName(){
return name;
}//end getName
/**
* <summary>
* This method will add a word
* </summary>
*/
private void addWord(string wordString){
wordCount++;
Word word = null;
if(!hash.ContainsKey(wordString)) {
word = new Word(wordString);
hash.Add(wordString, word);
list.Add(word);
word.FrequencyChanged +=
new Action<Word>(word_FrequencyChanged);
}//end if
else{
word = hash[wordString];
word.incrementFrequency();
}
}//end add word
/**
* <summary>
* This method is executed when the word frequency is updated
* </summary>
*/
public void word_FrequencyChanged(Word obj){
/*
* Resort using quick sort
* MSDN:
* On average, this method is an O(n log n) operation,
* where n is Count; in the worst case
* it is an O(n ^ 2) operation.
*/
list.Sort(obj);
}//end word
/**
* <summary>
* This method will generate a repsonse
* </summary>
*/
private void respond(){
//Only respond if necessary
int wordLength = (int)(minMessageSize +
(random.NextDouble() *
(maxMessageSize - minMessageSize)));
double runningTotal = 0.0d;
string message = "";
for(int i=0;i<wordLength;i++){
if(random.NextDouble() < chaos){
//Just pick a random word
message += list[(int)(random.NextDouble()
* list.Count)].getWord() + " ";
}
else{
double requiredOdds = random.NextDouble();
//Pick a word based on frequency
runningTotal = 0.0d;
foreach(Word w in list){
runningTotal += ((double)w.getFrequency()/
(double)wordCount);
if(runningTotal > requiredOdds){
message += w.getWord() + " ";
break;
}
}//end foreach
}
}//end for
if(message.Length > 0){
Message mm = new Message(this,
this.conversation, message);
conversation.speak(mm);
}
}//end respond
/**
* <summary>
* This method receive a new message
* </summary>
* <remarks>
* ParrotAlgorithm is a stupid parrot.
* He repeats what he hears most often
* </remarks>
*/
public void newMessage(Message message){
//ignore own messages
if(message.speaker != this){
string[] tokens =
message.message.Split(new char[]{' '});
if(tokens.Length > maxMessageSize)
maxMessageSize = tokens.Length;
foreach(string s in tokens){
addWord(s);
}//end foreach
respond();
}//end if
}//end new Message
/**
* <summary>
* This method will instruct this
* class to join a conversation
* </summary>
*/
public void joinConversation(Conversation conversation){
this.conversation = conversation;
conversation.addParticipant(this);
}//end joinConversation
/**
* <summary />
*/
public ParrotAlgorithm() {
instanceCount++;
}//end Constructor
}//end ParrotAlgorithm
The first bit of explanation is the Word class. What is it for, why did you do that? It wraps words or tokens in a message, and stores associated information such as frequency. The event FrequencyChanged is called whenever the frequency is updated, to allow the list to be resorted. This is probably not the most efficient method. Adding all of the new words and then sorting the list is, possibly, more efficient.
private void addWord(string wordString){
wordCount++;
Word word = null;
if(!hash.ContainsKey(wordString)) {
word = new Word(wordString);
hash.Add(wordString, word);
list.Add(word);
word.FrequencyChanged +=
new Action<Word>(word_FrequencyChanged);
}//end if
else{
word = hash[wordString];
word.incrementFrequency();
}
}//end add word
The above code will add a word to the memory banks. A dictionary object, which I call hash, is used to update the frequency for words that have already been used and stored. The list is a List<Word> object (.NET 2.0 Generics) that stores the words in sorted order. This is important for getting the random words later.
/**
* <summary>
* This method will generate a repsonse
* </summary>
*/
private void respond(){
//Only respond if necessary
int wordLength = (int)(minMessageSize +
(random.NextDouble() * (maxMessageSize - minMessageSize)));
double runningTotal = 0.0d;
string message = "";
for(int i=0;i<wordLength;i++){
if(random.NextDouble() < chaos){
//Just pick a random word
message += list[(int)(random.NextDouble() *
list.Count)].getWord() + " ";
}
else{
double requiredOdds = random.NextDouble();
//Pick a word based on frequency
runningTotal = 0.0d;
foreach(Word w in list){
runningTotal += ((double)w.getFrequency()/
(double)wordCount);
if(runningTotal > requiredOdds){
message += w.getWord() + " ";
break;
}
}//end foreach
}
}//end for
if(message.Length > 0){
Message mm = new Message(this, this.conversation, message);
conversation.speak(mm);
}
}//end respond
This method contains all of the brains behind the parrot.
int wordLength = (int)(minMessageSize +
(random.NextDouble() * (maxMessageSize - minMessageSize)));
Randomly select the length of the response. This code guarantees a minimum sentence size, and won't let the parrot respond with enormous sentences unless its peers do so as well.
if(random.NextDouble() < chaos){
Randomness is crucial. This is what really makes things interesting. There just has to be mutation. Eliminate this code or change the chaos value to see how it affects the results.
runningTotal += ((double)w.getFrequency()/(double)wordCount);
This function normalizes the value of the frequencies, making the sum of all frequencies == 1 (maybe). Therefore, you can loop, starting with the lowest frequency, and add up the normals to compare to the random number you desire. To be honest, I have not proved this statement, but it seems intuitively correct. Someone with a stats book and free-time is welcome to prove or disprove it for me. For the interim, it works for me.
Points of Interest
- A good place to find answers to hard problems: Google.
- The Turing Test.
- CAPTCHA's.