AI Knowledge Representation & Reasoning Course | IIT Madras Prof. Deepak Khemani
Course Details
| Exam Registration | 6516 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Computer Science and Engineering, Artificial Intelligence, Data Science |
| Credit Points | 3 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 17 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Artificial Intelligence: Knowledge Representation And Reasoning – A Deep Dive
At the heart of every intelligent system lies a fundamental capability: the ability to represent knowledge about its world and reason with it to solve problems. The course Artificial Intelligence: Knowledge Representation And Reasoning, offered by the prestigious Indian Institute of Technology Madras (IIT Madras), provides a rigorous exploration of this core pillar of AI. Led by the esteemed Prof. Deepak Khemani, this 12-week journey is designed to equip students with the theoretical foundations and practical skills needed to build articulate, problem-solving systems.
Meet the Instructor: Prof. Deepak Khemani
Prof. Deepak Khemani is a Professor in the Department of Computer Science and Engineering at IIT Madras. With a distinguished academic background including a PhD from IIT Bombay and research stints across Europe, Prof. Khemani brings decades of expertise to the table. His research is driven by a long-term vision to create AI systems that can seamlessly interact with humans. His interests span Memory Based Reasoning, Planning, Constraint Satisfaction, Qualitative Reasoning, and Natural Language Processing, making him the ideal guide for this complex subject.
Who is This Course For?
This course is meticulously designed for:
- Academic Students: BE, ME, MS, MSc, and PhD students in Computer Science, Artificial Intelligence, and Data Science.
- Prerequisites: A foundational understanding of Discrete Mathematics, Data Structures, formal languages, logic, and programming is recommended.
- Industry Professionals: Individuals in software companies focusing on knowledge-based systems, semantic web technologies, and semantic search will find the content highly applicable.
Course Overview: Building the Mind of an AI
The course serves as a companion to "Artificial Intelligence: Search Methods for Problem Solving" and delves into the symbolic heart of AI. It progresses from simple representational formalisms to advanced concepts for handling dynamic worlds and incomplete information.
Detailed 12-Week Course Layout
| Week | Topics Covered |
|---|---|
| Week 1 | Introduction, History, and Philosophy of AI & KR |
| Week 2 | Symbolic Reasoning: Truth, Logic, and Provability |
| Week 3 | Propositional Logic, Direct Proofs, The Tableau Method |
| Week 4 | First Order Logic, Universal Instantiation, Unification Algorithm |
| Week 5 | Forward/Backward Chaining, The Resolution Refutation Method |
| Week 6 | Horn Clauses, Logic Programming, and Prolog |
| Week 7 | Rule Based Systems, The OPS5 Language, The Rete Algorithm |
| Week 8 | Representation in FOL, Conceptual Dependency |
| Week 9 | Frames, Description Logics, Web Ontology Language (OWL) |
| Week 10 | Taxonomies, Inheritance, Default Reasoning |
| Week 11 | Advanced Topics: Circumscription, Auto-epistemic Reasoning, Event Calculus |
| Week 12 | Epistemic Logic: Representing Knowledge and Belief |
Why is Knowledge Representation & Reasoning Crucial?
Knowledge Representation (KR) is not just about storing data; it's about structuring information in a way that a computer can use to infer new facts, make decisions, and explain its actions. This course covers the evolution from simple propositional statements to sophisticated logics that can model actions, changes, situations, and the beliefs of other agents—essential for creating robust AI in uncertain, real-world environments.
Essential Reading Materials
The course is supported by a robust reading list to deepen your understanding:
Text Books:
- Brachman & Levesque: Knowledge Representation and Reasoning (2004) – A modern classic.
- Deepak Khemani: A First Course in Artificial Intelligence (2013) – The instructor's own comprehensive text.
Reference Books:
- Schank & Abelson: Scripts, Plans, Goals, and Understanding (1977) – Foundational work on knowledge structures.
- Antoniou & van Harmelen: A Semantic Web Primer (2008) – Connects KR to modern web technologies.
- Sowa: Knowledge Representation: Logical, Philosophical, and Computational Foundations (2000) – A deep, interdisciplinary perspective.
Industry Relevance and Applications
The principles taught in this course are the backbone of numerous cutting-edge technologies. Industry support comes from software companies engaged in:
- Semantic Web & Search: Enabling machines to understand the meaning of web content.
- Expert Systems & Decision Support: Building rule-based systems for diagnostics, planning, and configuration.
- Natural Language Processing: Providing the logical framework for understanding and generating language.
- Intelligent Agents & Robotics: Allowing autonomous systems to reason about their environment and actions.
Embark on this 12-week course to master the language of machine thought. Under the guidance of Prof. Deepak Khemani, you will gain the tools to move from data to knowledge, and from calculation to true reasoning—a critical step towards building the intelligent systems of tomorrow.
Enroll Now →