CS6380: Artificial Intelligence (Jul-Nov 2026)

Course Information

Offered for: BTech, MTech, MS, PhD

Teaching Assistants: Jignasa Bhunia
Dhrubajyoti Giri
Raju Kumar Gupta
Ravijyotisna Pachauri
Dudekula Abdul Rahiman
S Saathvik

Pre-requisite Skills: Familiarity with Logic, Programming, Analysis of Algorithms, and Probability

Grading Policy: Quiz 1: 20%
Quiz 2: 20%
End-semester: 30%
In class tasks: 5%
Quizzes based on Assignments: 25% (5 best out of 6 HW quizzes)

Course Email: pulkitv@cse.iitm.ac.in
Please start the subject line of all course-related emails with [CS6380].

Objectives

This course surveys classical artificial intelligence with emphasis on symbolic methods: agents, search, planning, constraint satisfaction, game playing, and logical inference.

Historical perspective, the Turing test, physical symbol systems, and the scope of symbolic AI
State-space, heuristic, randomized, and optimal-path search techniques
Problem decomposition, rule-based systems, adversarial search, and planning
Propositional and first-order logic, soundness and completeness, and chaining-based inference

The goal is to equip students to model problems in AI formalisms and to understand when and why standard algorithms apply.

Course Contents

Module 1: Introduction

Overview and historical perspective; state of the art
Intelligent agents: rationality, PEAS, and agent architectures

Module 2: Solving Problems by Searching

Problem-solving agents and problem formulation
Uninformed search: breadth-first, uniform-cost, depth-first, iterative deepening
Informed (heuristic) search: greedy best-first and A*
A* properties and heuristic design

Module 3: Search in Complex Environments

Local search: hill climbing, simulated annealing, genetic algorithms
Nondeterministic actions and AND–OR search
Partial observability and online search

Module 4: Adversarial Search and Games

Minimax algorithm
Alpha–beta pruning
Evaluation functions, cutting off search, and move ordering
Monte Carlo Tree Search
Stochastic and partially observable games

Module 5: Constraint Satisfaction Problems

CSP formulation
Constraint propagation and arc consistency
Backtracking search for CSPs
Inference, backjumping, local search, and problem structure

Module 6: Logical Agents and Propositional Logic

Knowledge-based agents and propositional logic
Entailment, model checking, and validity
Resolution, CNF, and Horn clauses
Forward and backward chaining; DPLL and SAT solvers

Module 7: First-Order Logic

Syntax and semantics of first-order logic
Using first-order logic
Inference: unification, forward chaining, and backward chaining
Resolution in first-order logic

Module 8: Knowledge Representation

Categories and objects
Events, reasoning systems, and default reasoning

Module 9: Classical Planning

PDDL and example domains
Planning algorithms and heuristics

Module 10: Philosophy, Ethics, and Safety of AI

Philosophy, ethics, and safety of AI

Text Books

The primary reference is Russell and Norvig. Additional readings may be linked from the course calendar as the semester progresses.

[AIMA] Artificial Intelligence: A Modern Approach, 4th Edition. Stuart Russell and Peter Norvig. Pearson, 2020. Course resources and errata: aima.cs.berkeley.edu.

[FCAI] A First Course in Artificial Intelligence, 1st Edition. Deepak Khemani. McGraw Hill Education (India), 2013. ISBN-10: 1259029980, ISBN-13: 9781259029981. mheducation.co.in.

Course Calendar

Mon, Jul 27

Introduction: What is AI? History and State of the Art

AIMA Sections 1.1–1.5