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.
More info

Teaching Assistant(s)

SOWMYA S SUNDARAM

Doctor of Philosophy
Department of Computer Science and Engineering
IIT Madras

 Course Duration : Jan-Apr 2017
  View Course
 Enrollment : 01-Jan-2017 to 23-Jan-2017
 Exam registration : 15-Feb-2017 to 27-Mar-2017
 Exam Date : 23-Apr-2017

Enrolled

4034

Registered

278

Certificate Eligible

147

Certified Category Count

Gold

0

Silver

0

Elite

14

Successfully completed

133

Participation

99

Success

Elite

Gold




Legend

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

Final Score Calculation Logic

  • Assignment Score = Average of best 8 out of 13 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

SATYA HARI PRASAD YALLA 84%

LINKEDIN


Top 2 % of Certified Candidates

B HARISH 77%

QUALCOMM INDIA PVT LTD

BHAVANA G DONGRE 69%

M S RAMAIAH INSTITUTE OF TECHNOLOGY

BONU NAGASHIRISHA 69%

MADANAPALLE INSTITUTE OF TECHNOLOGY & SCIENCE


Top 5 % of Certified Candidates

DHRUVA PATIL 68%

R V COLLEGE OF ENGINEERING

M SNEHA 68%

M S RAMAIAH INSTITUTE OF TECHNOLOGY

SHAIK FALAK 63%

MADANAPALLE INSTITUTE OF TECHNOLOGY & SCIENCE

Enrollment Statistics

Total Enrollment: -1

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Registration Statistics

Total Registration : 0

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Will be updated shortly.!
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.