Artificial Intelligence: Knowledge Representation and Reasoning

NPTEL and Indian Institute of Technology Madras via YouTube

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Provider

YouTube
Pricing

Free Video
Languages

English
Duration & workload

1 day 15 hours
Sessions

On-Demand
Level

Advanced

Found in

COURSE OUTLINE: 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.

Syllabus

Introduction.
Introduction to Knowledge Representation and Reasoning.
An Introduction to Formal Logics.
Propositional Logic: Language, Semantics and Reasoning.
Propositional Logic: Syntax and Truth Values.
Propositional Logic: Valid Arguments and Proof Systems.
Propositional Logic: Rules of Inference and Natural Deduction.
Propositional Logic: Axiomatic Systems and Hilbert Style Proofs.
Propositional Logic: The Tableau Method.
Propositional Logic: The Resolution Refutation Method.
Syntax.
Semantics.
Entailment and Models.
Forward Chaining.
Unification.
Proof Systems.
Forward Chaining Rule Based Systems.
The Rete Algorithm.
Rete Algorithm - Example.
The OPS5 Expert System Shell.
Programming in a Rule Based Language.
Skolemization.
Terminological Facts.
Properties and Categories.
Reification and Abstract Entities.
The Event Calculus: Reasoning About Change.
Resource Description Framework (RDF).
Natural Language Semantics.
CD Theory.
CD Theory (contd).
English to CD Theory.
Natural Language Semantics.
Backward Chaining.
Logic Programming.
Prolog.
Search in Prolog.
Controlling Search.
The Cut Operator in Prolog.
Incompleteness.
M7 Lec 2 - The Resolution Refutation method for First Order Logic.
Clause Form.
FOL with Equality.
Complexity of Resolution Refutation.
The Resolution Method for FOL.
Semantic Nets and Frames.
Scripts.
Applying Scripts.
Goals, Plans and Actions.
Plan Applier Mechanism.
Top Down and Bottom Up Reasoning.
Introduction.
Normalisation.
Structure Matching.
Structure Matching - Example.
Classification.
A-box reasoning.
DL: Extensions.
DL: ALC.
ALC examples.
Taxonomies and Inheritance.
Beliefs.
Inheritance Hierarchies:.
Event Calculus Revisited.
Minimal Models.
Circumscription (contd).
Circumscription.
Introduction..
Circumscription in EC.
Autoepistemc Logic.
Defaul Logic.
The Muddy Children Puzzle.
Epistemic Logic.

Taught by

Artificial Intelligence

Reviews

4.5 rating, based on 8 Class Central reviews

Start your review of Artificial Intelligence: Knowledge Representation and Reasoning

Deepak Bhist

The NPTEL course “Artificial Intelligence: Knowledge Representation and Reasoning” offers a clear and structured introduction to foundational AI concepts. It effectively explains how knowledge can be modeled, represented, and utilized for intelligent decision-making. The lectures are well-organized, with examples that make abstract ideas easier to grasp. Topics such as logic, inference, ontologies, and planning are presented in a practical and engaging manner. The assignments and quizzes reinforce understanding and encourage analytical thinking. Overall, this course is highly valuable for students seeking a strong conceptual grounding in AI and for anyone interested in learning how machines reason and solve problems systematically.
Sushovan Bandopadhyay

The Artificial Intelligence: Knowledge Representation and Reasoning course on NPTEL via YouTube is an excellent resource for beginners and intermediate learners. The concepts are explained clearly, with real-world examples that make complex ideas easier to understand. The instructor's delivery is systematic, and the course content is well-structured, covering logic, semantic networks, and reasoning techniques in detail. Highly recommended for anyone aiming to build a strong foundation in AI reasoning methods.
Muhammad Arya Fatthurahman

"This course provides a clear and structured introduction to knowledge representation and reasoning in AI. The instructor explains complex concepts like propositional logic, predicate logic, semantic networks, and reasoning techniques in a very accessible way. The examples and exercises help reinforce understanding, making it a great resource for both beginners and intermediate learners."
Jenitha

This was easy way to learn about the content and this subject is helpful to develop skills but I've completed the course but certification is not send to me
Priyanka Pathak

It was very helpful and I get to learn many things . I want more such Courses to learn more skills and gain knowledge
Soumya Maheshwari
1

Perfect course for begginers. It is very helpful for person seeking skills and sone kind of job.
The whole theory has been explained very simply and one can also take down very good notes throughout the class
Ganesan M

It's useful for my future invention and It may use to many others ...then ease improve more creative...
Deepak Sisodiya1503

Actually This course was amazing and this was full AI based subject.this content was really really good.