Artificial Intelligence: Knowledge Representation And Reasoning


Prof. Deepak Khemani

IIT Madras

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SKU: IIT Madras Category:


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.


BE/ME/MS/MSc/PhD students


Some exposure to formal languages, logic and programming


Software companies dealing with knowledge and reasoning, including the semantic web and semantic search.


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.

Certification Process

1. Join the course
Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 3000 + GST

2. Watch Videos+Submit Assignments
After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams
A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

Assignment score = Score more than 50% in at least 9/12 assignments.
Exam score = 50% of the proctored certification exam score out of 100
Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams
The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.

5. Results and Certification
After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.


Course Details

Week 1:  Introduction. History and Philosophy.
Week 2:  Symbolic Reasoning. Truth, Logic, and Provability.
Week 3:  Propositional Logic. Direct Proofs. The Tableau Method.
Week 4:  First Order Logic. Universal Instantiation. The Unification Algorithm.
Week 5:  Forward and Backward Chaining. The Resolution Refutation Method.
Week 6:  Horn Clauses and Logic Programming. Prolog.
Week 7:  Rule Based Systems. The OPS5 Language. The Rete Algorithm.
Week 8:  Representation in First Order Logic. Conceptual Dependency.
Week 9:  Frames. Description Logics and the Web Ontology Language
Week 10: Taxonomies and Inheritance. Default Reasoning.
Week 11: Circumscription. Auto-epistemic Reasoning. Event Calculus
Week 12: Epistemic Logic. Knowledge and Belief.

Books and References

Text Books

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.

Reference Books

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. Artif. Intell. 77(2), pp. 249-284, 1995.
4. Grigoris Antoniou and Frank van Harmelen, A Semantic Web Primer, 2nd Ed, MIT Press, 2008.
5. John F. Sowa: Conceptual Structures: Information Processing in Mind and Machine, Addison–Wesley Publishing Company, Reading Massachusetts, 1984.
6. John F. Sowa: Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks/Cole, Thomson Learning, 2000.


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