Course Name: AI: Knowledge Representation and Reasoning

Course abstract

An intelligent agent needs to be able to solve problems in its world. The ability to create representations of the domain of interest and reason with these representations is a key to intelligence. In this course we explore a variety of representation formalisms and the associated algorithms for reasoning. We start with a simple language of propositions, and move on to first order logic, and then to representations for reasoning about action, change, situations, and about other agents in incomplete information situations. This course is a companion to the course ?Artificial Intelligence: Search Methods for Problem Solving? that was offered recently and the lectures for which are available online.


Course Instructor

Media Object

Prof. Deepak Khemani

Deepak Khemani is Professor at Department of Computer Science and Engineering, IIT Madras. He completed his B.Tech. (1980) in Mechanical Engineering, and M.Tech. (1983) and PhD. (1989) in Computer Science from IIT Bombay, and has been with IIT Madras since then. In between he spent a year at Tata Research Development and Design Centre, Pune and another at the youngest IIT at Mandi. He has had shorter stays at several Computing departments in Europe. Prof Khemani’s long-term goals are to build articulate problem solving systems using AI that can interact with human beings. His research interests include Memory Based Reasoning, Knowledge Representation and Reasoning, Planning and Constraint Satisfaction, Qualitative Reasoning and Natural Language Processing.


Teaching Assistant(s)

Shikha Singh

PhD, Computer Science and Engineering

 Course Duration : Jan-Apr 2019
  View Course
 Enrollment : 15-Nov-2018 to 28-Jan-2019
 Exam registration : 28-Jan-2019 to 19-Apr-2019
 Exam Date : 27-Apr-2019

Enrolled

13789

Registered

739

Certificate Eligible

451

Certified Category Count

Gold

0

Silver

9

Elite

63

Successfully completed

379

Participation

173

Success

Elite

Silver

Gold




Legend

>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed
<40 - No Certificate

Final Score Calculation Logic

  • Assignment Score = Average of best 8 out of 12 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
AI: Knowledge Representation and Reasoning - Toppers list
Top 1 % of Certified Candidates

V DIVYA 81%

INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, DESIGN AND MANUFACTURING, KANCHEEPURAM

SUJA RAMACHANDRAN 80%

HOMI BHABHA NATIONAL INSTITUTE

MAYANK KAUSHIK 78%

KAMLA NEHRU INSTITUTE OF TECHNOLOGY

T.JAHNAVI 78%

MADANAPALLE INSTITUTE OF TECHNOLOGY & SCIENCE

SITARA K 77%

MADANAPALLE INSTITUTE OF TECHNOLOGY & SCIENCE


Top 2 % of Certified Candidates

GADDE SRIDEVI 75%

RAGHU ENGINEERING COLLEGE

ROHAN DEB 75%

NATIONAL INSTITUTE OF TECHNOLOGY, SILCHAR

R. S. SHUDAPREYAA 75%

KARPAGAM INSTITUTE OF TECHNOLOGY

PAMIR ROY 75%

NERIST


Top 5 % of Certified Candidates

M.JYOSHMITHA 73%

MADANAPALLE INSTITUTE OF TECHNOLOGY & SCIENCE

SUDHANSHU KUMAR 73%

CENTRAL UNIVERSITY OF KARNATAKA

PARLAPALLI RUPA 72%

MADANAPALLE INSTITUTE OF TECHNOLOGY & SCIENCE

RAJEEV KUMAR 72%

DRDO

BODAKA.V.RAMYA 71%

MADANAPALLE INSTITUTE OF TECHNOLOGY & SCIENCE

ANKIT KUMAR SINGH 71%

KAMLA NEHRU INSTITUTE OF TECHNOLOGY

KURAL 70%

HARCOURT BUTLER TECHNICAL UNIVERSITY

SACHIN KUMAR 70%

KAMLA NEHRU INSTITUTE OF TECHNOLOGY

RAGHAV AGARWAL 70%

KAMLA NEHRU INSTITUTE OF TECHNOLOGY

PRATIK KUMAR SAHOO 69%

S.R.M. INSTITUTE OF SCIENCE AND TECHNOLOGY

SHAILESH KUMAR 69%

KAMLA NEHRU INSTITUTE OF TECHNOLOGY

K V VINITHA 69%

MADANAPALLE INSTITUTE OF TECHNOLOGY & SCIENCE

ABDUL ALEEM 69%

KAMLA NEHRU INSTITUTE OF TECHNOLOGY

NILESH KUMAR SINGH 69%

KAMLA NEHRU INSTITUTE OF TECHNOLOGY

Enrollment Statistics

Total Enrollment: -1

Data Not Found..!
Data Not Found..!

Registration Statistics

Total Registration : 739

Assignment Statistics



Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.