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Virtual intelligent student counselor for apiit
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Virtual intelligent student counselor for apiit

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Final year project presentation

Final year project presentation

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  • The era of technological and scientific evolutionMan tries to automate the tasks done by humans inventing the new machineries and applicationsY don’t we user the similar concept to reduce the work load of the APIIT counselors and make it easy for the prospective students to retrieve necessary information regarding APIIT degree courses easilyThat’s all bout my final year project
  • *Counseling plays a major role in any university*APIIT counseling two major aspects*growth of the student population is 25% per year and the information requests*handled by only 3-4 counselors*some need more clarifications some don’t*specifically come to apiit premises
  • Elizabeth*Adding, modifying and deleting scripts while conversation in progress*Both stores the previous output
  • A link to a PDF file will be provided for a clear view of the design
  • Transcript

    • 1. By Umesha Gunasinghe CB002789 05/01/11
    • 2. Overview Problem Background Key Decisions Made Design Development Test Results 2/27
    • 3. Problem Background Issues  Growth of student the population  Travelling issues  Cost  Time  Counselors have to answer the general questions every time 3/27
    • 4. Proposed Solution Web-based Virtual Counselor  Advantages  Accessibility  Vast coverage of target audience  Saves Time  Saves Cost  Reduces the counselors workload 4/27
    • 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 5/27
    • 6. Key Decisions Made AIML language for the knowledgebase development ALICE’s input normalization for the input normalization ALICE’s pattern matching algorithm 6/27
    • 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> 8/27
    • 9. AIML Samples  Recursive Category <category> <pattern>* COURSES * AVAILABLE *</pattern> <template> <srai>WHAT ARE THE DEGREE COURSES AVAILABLE AT APIIT</srai> </template> </category> 9/27
    • 10. Decisions Made-Response Generation 10/27 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 11/27
    • 12. Example  Input Path Creation 12/27
    • 13. Pattern Matching Algorithm 13/27
    • 14. Example  Pattern Matching 14/27
    • 15. Special and Additional Features Special  Spell Check  Similar Wordset check  Synonyms Check Additional  Temporary Conversation Log  Admin Module 15/27
    • 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 16/27
    • 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 17/27
    • 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 18/27
    • 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 19/27
    • 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 20/27
    • 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. 21/27
    • 22. Test Results  Validation Test 23/27 0% 20% 40% 60% 80% 100% Round 1 Round 2 No answer Answer
    • 23. Test Results  Unit Tests (7 main methods)  System Test 22/27 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 24/27
    • 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 25/27
    • 26. Do you want to know more? 26/27
    • 27. Thank You 27/27

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