Transcript ppt - CSE, IIT Bombay

CHATBOTS
GROUP-8
RaviKant Narayan
Gaurav Jain
Introduction

Interaction is in natural language

No use of formal languages
Revolutionary step in the field of
human-machine interaction


Artificial Conversational agents
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
Motivation
Use at places like railway enquiry

psychotherapy
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Play MUDs,tell stories
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How it all started
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The Turing test -The Imitation game
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PARRY-Artificial paranoia
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The Turing test as a simulation validator
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The Loebner Prize
Elizabeth
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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
N: No match
O: Output transformation
&: Action to be perform
ELIZABETH
Knowledge stored as script in text file
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Script file – 4 parts
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Part 1 – Script command line
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Holds welcome, void and no keyword messages
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Part 2 – Input transformation rules
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Part 3 – Output transformation rules
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Part 4 – Keyword patterns
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Example
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Input Transformation rule
I MUM => mother
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Output Transformation rule
O my => YOUR
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Keyword Transformation rule
K I LIKE [string]ING
R HAVE YOU [string]ED AT ALL RECENTLY?
Dynamic Processing
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Modification of script while conversing
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Adding, Memorization, deleting
New scripts could be added while ongoing
conversation
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Responses are kept at a hold for future
matching
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Script commands can be deleted
Pattern matching algorithm
Firstly some preparations done for pattern
matching algorithm(like removing illegal
characters)

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Then five stages
Stage 1 - All input transformation rules applied
one by one
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Stage 2 – Keyword pattern matching
Pattern matching algorithm contd
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Stage 3 – Output transformation rules applied
Stage 4 – In case no match found in stage 2, it
gives void or no keyword message
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Stage 5 – Dynamic processes performed if
required
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ALICE
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Artificial Linguistic Internet Computer Entity
Has knowledge about patterns in English
conversation

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The storage is done in AIML files
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A derivative of XML
AIML
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Artificial Intelligence Markup Language
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AIML objects(data objects)
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Fundamentals units are Topics and Categories
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Topic – Like a node
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Has a name and a list of related categories
continued
Category – A rule for giving outputs based on
given input patterns
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Contains a pattern(user input) and a
template(represents possible output)
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Categories types
Atomic Categories :- No wildcard symbols
(_ and *) in the patterns
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Default categories :- Patterns have wild card
symbols
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Recursive categories :- Templates contain srai
and sr tags
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Srai :- Simply recursive artificial intelligence
Continued
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Sr :- Symbolic Reduction
Reducing complex grammatical forms to
simpler ones

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Dealing with synonyms
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Divide and Conquer.
Pattern Matching in ALICE
Requires Preparation
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Normalization Process
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Three step procedure
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Normalization Process
Three steps
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Substitution Normalization
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Sentence Splitting Normalization
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Pattern filling Normalization
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Example
Input - “ I do not know. Do you, or will you,
have a robots.txt file”
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Step1 - “I do not know. Do you, or will you,
have a robots dot txt file”
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Information susceptible to loss in next steps
is retained
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Continued
Step2 - “I do not know.” “Do you, or will you,
have a robots dot txt file”
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
The input split into sentences
Step3 - “I DO NOT KNOW” “DO YOU OR WILL
YOU HAVE A ROBOTS DOT TXT FILE”
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Punctuation removal
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Conversion to upper case
Pattern matching algorithm
The algorithm is similar to that used in
Graphmaster set of files and directories

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First the preperation work is done
Then a depth first search combined with
backtracking is used to get a matching
pattern
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Example
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user inputs”halo what is 2 and 2”
Converted to :Sentence 1 – Halo
Sentence 2 – WHAT IS 2 AND 2
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Atomic pattern match found for HALO as
HELLO
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Dealing with synonyms
continued
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Template available for WHAT IS 2 *
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Backtracking applied
Pattern match found and response selected
randomly from list of responses
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Answer is “Hi there! Four.”
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>
</category>
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>
</category>
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.
Tricks
Chatbots use a lot of them for a
more human effect
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Some use a trick of deduction
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To let the user speak first and then
reply from the deduction made from
that
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Tricks Contd
Some use the approach of slowing
down
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The chatbot takes a specific
amount of time to reply as a human
would
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Making seemingly random
mistakes intentionally
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Tricks Contd
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Can say I dont know
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launch a new story
Why do you ask that?, which is really a way of
changing the subject.

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)

Continued
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ITS :- Intelligent Tutoring System
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Chatbots in foreign language learning
The system uses a communication
response mechanism

The learning is motivated by the personality
of the learner
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Social engineering
Conclusion
Chatbots have wide range of applications
like artificial tutoring, social networking etc.

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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
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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.http://en.wikipedia.org/wiki/Chatbots