1. GROUP-8 RaviKant Narayan Gaurav Jain
CHATBOTS
GROUP-8
RaviKant Narayan
Gaurav Jain

2. Introduction Interaction is in natural language
No use of formal languages
Revolutionary step in the field of human-machine interaction
Artificial Conversational agents

3. Example chat-ELIZA Men are all alike. IN WHAT WAY?
They're always bugging us about something or other.
CAN YOU THINK OF A SPECIFIC EXAMPLE?
Well, my boyfriend made me come here.
YOUR BOYFRIEND MADE YOU COME HERE
He says I'm depressed much of the time.
I AM SORRY TO HEAR YOU ARE DEPRESSED

4. Motivation
Use at places like railway enquiry
psychotherapy
Play MUDs,tell stories

5. How it all started The Turing test -The Imitation game
PARRY-Artificial paranoia
The Turing test as a simulation validator
The Loebner Prize

6. Elizabeth Script Command Notations W: Welcome message
Q: quitting message
N: No match
V: Void input
I: Input transformation
/ : Comment
K: Key word pattern
R: key word response
M: Memorise phrase
O: Output transformation
&: Action to be perform

7. ELIZABETH Knowledge stored as script in text file
Script file – 4 parts
Part 1 – Script command line
Holds welcome, void and no keyword messages
Part 2 – Input transformation rules
Part 3 – Output transformation rules
Part 4 – Keyword patterns

8. Example Input Transformation rule Output Transformation rule
I MUM => mother
Output Transformation rule
O my => YOUR
Keyword Transformation rule
K I LIKE [string]ING
R HAVE YOU [string]ED AT ALL RECENTLY?

9. Dynamic Processing Modification of script while conversing
Adding, Memorization, deleting
New scripts could be added while ongoing conversation
Responses are kept at a hold for future matching
Script commands can be deleted

10. Pattern matching algorithm
Firstly some preparations done for pattern matching algorithm(like removing illegal characters)‏
Then five stages
Stage 1 - All input transformation rules applied one by one
Stage 2 – Keyword pattern matching

11. Pattern matching algorithm contd
Stage 3 – Output transformation rules applied
Stage 4 – In case no match found in stage 2, it gives void or no keyword message
Stage 5 – Dynamic processes performed if required

12. ALICE Artificial Linguistic Internet Computer Entity
Has knowledge about patterns in English conversation
The storage is done in AIML files
A derivative of XML

13. AIML Artificial Intelligence Markup Language
AIML objects(data objects)‏
Fundamentals units are Topics and Categories
Topic – Like a node
Has a name and a list of related categories

14. continued
Category – A rule for giving outputs based on given input patterns
Contains a pattern(user input) and a template(represents possible output)‏

15. Categories types
Atomic Categories :- No wildcard symbols (_ and *) in the patterns
Default categories :- Patterns have wild card symbols
Recursive categories :- Templates contain srai and sr tags
Srai :- Simply recursive artificial intelligence

16. Continued Sr :- Symbolic Reduction
Reducing complex grammatical forms to simpler ones
Dealing with synonyms
Divide and Conquer.

17. Pattern Matching in ALICE
Requires Preparation
Normalization Process
Three step procedure

18. Normalization Process
Three steps
Substitution Normalization
Sentence Splitting Normalization
Pattern filling Normalization

19. Example
Input - “ I do not know. Do you, or will you, have a robots.txt file”
Step1 - “I do not know. Do you, or will you, have a robots dot txt file”
Information susceptible to loss in next steps is retained

20. Continued
Step2 - “I do not know.” “Do you, or will you, have a robots dot txt file”
The input split into sentences
Step3 - “I DO NOT KNOW” “DO YOU OR WILL YOU HAVE A ROBOTS DOT TXT FILE”
Punctuation removal
Conversion to upper case

21. Pattern matching algorithm
The algorithm is similar to that used in Graphmaster set of files and directories
First the preperation work is done
Then a depth first search combined with backtracking is used to get a matching pattern

22. Example user inputs”halo what is 2 and 2”
Converted to : Sentence 1 – Halo Sentence 2 – WHAT IS 2 AND 2
Atomic pattern match found for HALO as HELLO
Dealing with synonyms

23. continued Template available for WHAT IS 2 * Backtracking applied
Pattern match found and response selected randomly from list of responses
Answer is “Hi there! Four.”

24. continued (1) <category>
<pattern>_ WHAT IS 2 AND 2</pattern>
<template> <sr/> <srai>WHAT IS 2 AND 2</srai> </template>
</category>
(2) <category>
<pattern>WHAT IS 2 *</pattern>
<template> <random> <li>Two.</li> <li>Four.</li> <li>Six.</li> <li>12.</li> </random>
</template>

25. continued (3) <category> <pattern>HALO</pattern>
<template> <srai>HELLO</srai> </template>
</category>
(4) <category>
<pattern>HELLO</pattern>
<template> <random> <li>Well hello there!</li> <li>Hi there!</li>
<li>Hi there. I was just wanting to talk</li> <li>Hello there !</li> </random>
</template>

26. How alice responded to complex sentences
Human:I will now give you a very comlex sentence and you have to figure out what does it mean
ALICE: Would you recommend this action to a friend?
Human: what would you do if you were left stranded in very lonely island away from all human beings with nobody to speak to ,with only a handful of clothes and food
ALICE: I will let you know when I become a left stranded in very lonely island away from all human beings with nobody to speak to with only a handful of clothes and food.

27. Tricks
Chatbots use a lot of them for a more human effect
Some use a trick of deduction
To let the user speak first and then reply from the deduction made from that

28. Tricks Contd
Some use the approach of slowing down
The chatbot takes a specific amount of time to reply as a human would
Making seemingly random mistakes intentionally

29. Tricks Contd Can say I dont know launch a new story
Why do you ask that?, which is really a way of changing the subject.

30. Applications
A large number of them possible
Use at places like railway enquiry
Saves lot of valuable human resources
As a psychiatrist(hard to find a good listener these days)‏

31. Continued
ITS :- Intelligent Tutoring System
Chatbots in foreign language learning
The system uses a communication response mechanism
The learning is motivated by the personality of the learner
Social engineering

32. Conclusion
Chatbots have wide range of applications like artificial tutoring, social networking etc.
Come handy in case of call centers
Most aspects of chatbots are pretty well developed today
Need to do a lookout against mean users who tend to take undue advantage of the facility

33. References
1. Ayse Pinar Saygin1,Ilyas Cicekli & Varol Akman,Turing Test:50 years later,Department of Cognitive Science,University of California,San Diego;Department of Computer Engineering,Bilkent University,Bilkent, 2000
2. Bayan Abu Shawar,Eric Atwell ,Using Dialogue Corpora to train a chatbot, School of computing University of leeds England, 2002
3. H. Chad Lane,Intelligent tutoring systems: Prospect for Guided Practice and Efficient Learning Institute for creative technologies, University of southern California, 2006 4.