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)

No teaching assistant data available for this course yet
 Course Duration : Jan-Apr 2020

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 Enrollment : 18-Nov-2019 to 03-Feb-2020

 Exam registration : 16-Dec-2019 to 20-Mar-2020

 Exam Date : 25-Apr-2020

Enrolled

9572

Registered

45

Certificate Eligible

11

Certified Category Count

Gold

0

Silver

0

Elite

1

Successfully completed

10

Participation

22

Success

Elite

Silver

Gold





Legend

AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75 AND FINAL SCORE >=40
BASED ON THE FINAL SCORE, Certificate criteria will be as below:
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed

Final Score Calculation Logic

  • Assignment Score = Average of best 8 out of 11 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
Artificial Intelligence : Knowledge Representation And Reasoning - Toppers list

RHUGVED PANKAJ CHAUDHARI 70%

GOVERNMENT POLYTECHNIC, PUNE

Enrollment Statistics

Total Enrollment: 9572

Registration Statistics

Total Registration : 309

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.