Course Name: Artificial Intelligence: 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)

Sowmya S Sundaram

Ph.D. Scholar,
Computer Science and Engineering
IIT MAdras

SHASHANK SHEKHAR

IIT Madras

 Course Duration : Jan-Apr 2016
  View Course
 Enrollment : 05-Dec-2015 to 04-Feb-2016
 Exam Date : 24-Apr-2016

Enrolled

8223

Registered

237

Certificate Eligible

179

Certified Category Count

Gold

2

Silver

0

Elite

15

Successfully completed

66

Participation

96

Success

Elite

Gold




Legend

>=90 - Elite+Gold
60-89 - Elite
35-59 - Successfully Completed
<=34 - Certificate of Participation

Final Score Calculation Logic

  • Assignment Score = Average of best 10 out of 13 assignments.
  • Final Score(Score on Certificate)= 50% of Exam Score + 50% of Assignment Score.
Artificial Intelligence: Knowledge Representation and Reasoning - Toppers list

ARUP BARUAH 98%

ASSAM DON BOSCO UNIVERSITY

NAVIN CHANDAR JACOB 95%

INAUTIX TECHNOLOGIES INDIA PVT LTD

SAROJ BALA 78%

AJAY KUMAR GARG ENGINEERING COLLEGE

GAURAV KUMAR SINHA 73%

VIVO COLLABORATION

DR SUNITA YADAV 71%

AJAY KUMAR GARG ENGINEERING COLLEGE

UMAMAGESWARAN J 70%

RMK Engineering College

EBENEZER RAJIV HINSTON PRABHU I 70%

ANNA UNIVEERSITY

PRASANNA G 70%

RMK Engineering College

SRINATH RAJU 67%

RMK Engineering College

VAIBHAV VIJAY VAIDYA 63%

INDIRA COLLEGE OF COMMERCE AND SCIENCE

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.