Final year project presentation

1. By Umesha Gunasinghe
CB002789
05/01/11

2. Overview
Problem Background
Key Decisions Made
Design
Development
Test Results
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3. Problem Background
Issues
 Growth of student the population
 Travelling issues
 Cost
 Time
 Counselors have to answer the general questions every
time
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4. Proposed Solution
Web-based Virtual Counselor
 Advantages
 Accessibility
 Vast coverage of target audience
 Saves Time
 Saves Cost
 Reduces the counselors workload
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5. Domain Analysis
Aims of the Questionnaire
 Whether the proposed system is useful
 If so what are the features that should be included
 What kind of information is needed for the system
Results(based on 76 students)
 Visited APIIT Website in search for more details 72%
 Information available on APIIT website : clear 46%, not
clear 47%
 85% said that it would be useful if there was a chat
system
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6. Key Decisions Made
AIML language for the knowledgebase development
ALICE’s input normalization for the input
normalization
ALICE’s pattern matching algorithm
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7. 7/27
AIML Scripting
•Knowledgebase writing style
with <category>
•Rich set of AIML tags
•Recursion using <srai> tag
•Categorization with <topic> tag
•Non intuitive input and output
rules
•Complexity and possibility of
error is high
•No recursion and few set of rule
categories
•Mainly based on keyword patterns
Decisions Made-Knowledgebase

8. AIML Samples
 Atomic Category
<category>
<pattern>WHAT IS THE TOTAL COST OF A DEGREE PROGRAMME AT
APIIT</pattern>
<template>It is around 1.3 million.*</template>
</category>
 Default Category
<category>
<pattern>* COURSES * AVAILABLE *</pattern>
<template>
<srai>WHAT ARE THE DEGREE COURSES AVAILABLE AT APIIT</srai>
</template>
</category>
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9. AIML Samples
 Recursive Category
<category>
<pattern>* COURSES * AVAILABLE *</pattern>
<template>
<srai>WHAT ARE THE DEGREE COURSES AVAILABLE AT APIIT</srai>
</template>
</category>
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10. Decisions Made-Response Generation
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ElizabethALICE
•Well defined normalization
process
•Simple pattern matching
algorithm depends on depth first
search
•Partitioning and combination
•Efficient
1. underscore match
2. Atomic word
3. ‘*’ key match
•Not a well defined normalization
process
•Complex process with 5 phases
•Key word matching(gives response
to the first keyword pattern
matched)
•No partitioning and combination
•Inefficient (all the input/output
patterns and keywords should be
checked)

11. Example
 Normalization Steps
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12. Example
 Input Path Creation
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13. Pattern Matching Algorithm
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14. Example
 Pattern Matching
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15. Special and Additional Features
Special
 Spell Check
 Similar Wordset check
 Synonyms Check
Additional
 Temporary Conversation Log
 Admin Module
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16. System Requirements
 Core System Requirement
 The system should be able to answer the user questions
accordingly in the scope of the knowledgebase.
 Special Features
 Spell, Similar Wordset and Synonyms Check
 Additional Features
 Temporary Conversation Log
 Admin Module
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17. Design-Overall Architecture
Reasoning
Knowledgebase
Response
Generation
Web Interface
User Query
System Output
Input query
System Response
Selection of the best
match
Best
matching
answerTemporary
Conversation Log
Spell check Synonym check
Similar wordset
check
Admin Module
Admin Controls
New Knowledge
addition
Edit the
knowledgebase
Other admin
controls
Database
Counselor Module
Request
for
synonym
check
Synonym
replaced user
input
Request
for spell
check
Spelling
checked
user input
Checked for
previous user
questions
Check
confirmation
Request
for similar
wordset
check
Similar
wordset
checked
user input
Request for the
best matching
answer
Saving the questions that could not be
answered by the system according to the
existing knowledge
New additions to the
knowledgebase
Editing the existing
knowledgebase
answers
Request for the
unanswered
user questions Unanswered
user questions
Admin request for other
necessary data
System data according
to the request
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18. Development
Followed Incremental Methodology (10 increments)
Knowledgebase implemented using AIML
Pattern matching and response generation
 Usage of AIML interpreter in C#
Spell Check
 Usage of Google Spell Checker API
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19. Similar Wordset Check
 Limited word set selected
Synonyms Check
 Usage of Wordnet and C# Wordnet API
 Brill Tagger API
Temporary Conversation Log
 Saves 10 recent questions and answers
Development
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20. Counselor Module
 Integration of Spell Check, Similar wordset Check,
Synonyms Check and Conversation Log
Admin Module Functionalities
 Edit knowledgebase
 Add new knowledge
 Add words to the similar wordset
Development
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21. Test Plan
 The unit tests were performed, to test the main 7 units
(most important methods of the classes) of the
system.
 The system test was performed to test the overall
performance of the system. Collected 100 user
question were used to perform this test.
 The validation test was performed for the counselor
module involving two counselors of APIIT for an
overall performance check in two stages.
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22. Test Results
 Validation Test
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0%
20%
40%
60%
80%
100%
Round 1 Round 2
No answer
Answer

23. Test Results
 Unit Tests (7 main methods)
 System Test
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System Test
Successfully
Answered(83%)
No direct
answer(5%)
Failed(12%)

24. Critical Appraisal
 Advantage in the usage of special AIML tags
(<srai>,<topic>)
 Introduction of Similar Wordset Check to reduce the
knowledge that should be written.
 Usage of “*” in pattern matching sometimes increases
ambiguity
 The Order of reasoning for the efficiency in the system
response
 System improves with the increase of the knowledge
with the help of the Admin Module
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25. Limitations and Enhancements
 Limitations
 Limited Knowledgebase
 Temporary Conversation Log (10)
 Spell Check(only 1 suggestion)
 Further Enhancements
 Persona-Type feature
 User Analysis base
 Chat Session Report
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26. Do you want to know more?
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27. Thank You
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