Artificial Intelligence: Knowledge Representation and Reasoning

By Prof. Deepak Khemani   |   IIT Madras
Learners enrolled: 13080
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. 

PREREQUISITES : Some exposure to formal languages, logic and programming 
INDUSTRY SUPPORT : Software companies dealing with knowledge and reasoning, including the semantic web and semantic search.
Course Status : Ongoing
Course Type : Elective
Duration : 12 weeks
Start Date : 18 Jan 2021
End Date : 09 Apr 2021
Exam Date : 24 Apr 2021
Enrollment Ends : 01 Feb 2021
Category :
  • Computer Science and Engineering
  • Artificial Intelligence
  • Data Science
Level : Undergraduate/Postgraduate

Course layout

Week 1: Introduction, Propositional Logic, Syntax and Semantics 
Week 2: Proof Systems, Natural Deduction, Tableau Method, Resolution Method 
Week 3: First Order Logic (FOL), Syntax and Semantics, Unification, Forward Chaining 
Week 4: The Rete Algorithm, Rete example, Programming Rule Based Systems 
Week 5: Representation in FOL, Categories and Properties, Reification, Event Calculus
Week 6: Deductive Retrieval, Backward Chaining, Logic Programming with Prolog 
Week 7: Resolution Refutation in FOL, FOL with Equality, Complexity of Theorem Proving 
Week 8: Description Logic (DL), Structure Matching, Classification 
Week 9: Extensions of DL, The ALC Language, Inheritance in Taxonomies 
Week 10: Default Reasoning, Circumscription, The Event Calculus Revisited 
Week 11: Default Logic, Autoepistemic Logic, Epistemic Logic, Multi Agent Scenarios

Optional Topics A: Conceptual Dependency (CD) Theory, Understanding Natural Language 
Optional Topics B: Semantic Nets, Frames, Scripts, Goals and Plans 

Books and references

Books followed in the course:
  1. Ronald J. Brachman, Hector J. Levesque: Knowledge Representation and Reasoning, Morgan Kaufmann, 2004.
  2. Deepak Khemani. A First Course in Artificial Intelligence, McGraw Hill Education (India), 2013. 
Supplementary Reading:
  1. Schank, Roger C., Robert P. Abelson: Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Hillsdale, NJ: Lawrence Erlbaum, 1977.
  2. R. C. Schank and C. K. Riesbeck: Inside Computer Understanding: Five Programs Plus Miniatures, Lawrence Erlbaum, 1981.
  3. Murray Shanahan: A Circumscriptive Calculus of Events. Artificial Intelligence 77(2), pp. 249-284, 1995.
  4. John F. Sowa: Conceptual Structures: Information Processing in Mind and Machine, Addison–Wesley Publishing Company, Reading Massachusetts, 1984.
  5. John F. Sowa: Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks/Cole, Thomson Learning, 2000. 

Instructor bio

Prof. Deepak Khemani

IIT Madras
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.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 24 April 2021 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.


Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Madras .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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