CS6380: Artificial Intelligence

Jul - Nov 2026

Course Information

Offered for: BTech, MTech, MS, PhD
Instructor: Pulkit Verma
Teaching Assistant: To be announced
Room: CS 15, CSB (Ground floor)
Class Times:
Monday: 02:00 - 03:15 PM
Tuesday: 03:30 - 04:45 PM
Thursday: 05:00 - 05:50 PM
Pre-requisite Skills: Familiarity with Logic, Programming, Analysis of Algorithms, and Probability
Grading Policy:
To be announced
Course Email: cs6380.jn26@gmail.com

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
  • Advanced planning (overview)

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.

Course Calendar

# Date Lecture Material Readings HW
1. Mon, Jul 27 Introduction: What is AI? History and State of the Art — AIMA Sections 1.1–1.5 -
2. Tue, Jul 28 Intelligent Agents: Rationality, PEAS, and Agent Architectures — AIMA Sections 2.1–2.4 -
3. Thu, Jul 30 Problem-Solving Agents and Problem Formulation — AIMA Sections 3.1–3.2 -
4. Mon, Aug 03 Uninformed Search: BFS, Uniform-Cost, and DFS — AIMA Sections 3.3–3.4 -
5. Tue, Aug 04 Iterative Deepening; Informed Search: Greedy Best-First and A* — AIMA Sections 3.4–3.5 -
6. Thu, Aug 06 A* Properties and Heuristic Design — AIMA Sections 3.5–3.6 HW1 out
7. Mon, Aug 10 Local Search: Hill Climbing, Simulated Annealing, Genetic Algorithms — AIMA Sections 4.1–4.2 -
8. Tue, Aug 11 Nondeterministic Actions and AND–OR Search — AIMA Section 4.3 -
9. Thu, Aug 13 Problem Session: Search — AIMA Chapters 3–4 HW1 solution out
10. Mon, Aug 17 Partial Observability and Online Search — AIMA Sections 4.4–4.5 -
11. Tue, Aug 18 Game Playing: Minimax — AIMA Sections 5.1–5.2 -
12. Thu, Aug 20 Minimax Worked Examples — AIMA Section 5.2 HW-Quiz 1; HW2 out
13. Mon, Aug 24 Alpha–Beta Pruning — AIMA Sections 5.2–5.3 -
14. Tue, Aug 25 Evaluation Functions, Cutting Off Search, and Move Ordering — AIMA Section 5.3 -
15. Thu, Aug 27 Problem Session: Minimax and Alpha–Beta (Quiz 1 prep) — AIMA Sections 5.1–5.3 HW2 solution out
— Mon, Aug 31 Quiz 1 (Ch. 1–4 and Sections 5.1–5.3) — — —
16. Tue, Sep 01 Monte Carlo Tree Search; Stochastic and Partially Observable Games — AIMA Sections 5.4–5.7 -
17. Thu, Sep 03 Games Recap and Q&A — AIMA Chapter 5 HW-Quiz 2; HW3 out
18. Mon, Sep 07 Constraint Satisfaction Problems — AIMA Section 6.1 -
19. Tue, Sep 08 Constraint Propagation and Arc Consistency — AIMA Section 6.2 -
20. Thu, Sep 10 Backtracking Search for CSPs — AIMA Section 6.3 HW3 solution out
— Mon, Sep 14 No class — Institute Holiday — — —
21. Tue, Sep 15 Advanced CSP: Inference, Backjumping, Local Search, Structure — AIMA Sections 6.3–6.5 -
22. Thu, Sep 17 CSP Problem Discussion — AIMA Chapter 6 HW-Quiz 3; HW4 out
23. Mon, Sep 21 Logical Agents and Propositional Logic — AIMA Sections 7.1–7.4 -
24. Tue, Sep 22 Entailment, Model Checking, and Validity — AIMA Sections 7.4–7.5 -
25. Thu, Sep 24 Resolution, CNF, and Horn Clauses — AIMA Section 7.5 HW4 solution out
26. Mon, Sep 28 Forward/Backward Chaining; DPLL and SAT Solvers — AIMA Sections 7.5–7.6 -
27. Tue, Sep 29 Propositional Agents (brief); First-Order Logic: Syntax and Semantics — AIMA Sections 7.7, 8.1–8.2 -
— Thu, Oct 01 No class — — HW5 out
28. Mon, Oct 05 Using First-Order Logic — AIMA Sections 8.3–8.4 HW-Quiz 4
29. Tue, Oct 06 Inference in FOL: Unification and Forward Chaining — AIMA Sections 9.1–9.3 -
30. Thu, Oct 08 Backward Chaining and Logic Programming — AIMA Section 9.4 HW5 solution out
— Mon, Oct 12 Quiz 2 (Ch. 7–9 through Section 9.4) — — —
31. Tue, Oct 13 Resolution in First-Order Logic — AIMA Section 9.5 -
32. Thu, Oct 15 Knowledge Representation: Categories and Objects (short intro) — AIMA Sections 10.1–10.2 HW-Quiz 5; HW6 out
33. Mon, Oct 19 KR: Events, Reasoning Systems, and Default Reasoning — AIMA Sections 10.3–10.6 -
— Tue, Oct 20 No class — Institute Holiday — — —
34. Thu, Oct 22 Classical Planning: PDDL and Example Domains — AIMA Section 11.1 HW6 solution out
35. Mon, Oct 26 Classical Planning: Algorithms and Heuristics — AIMA Sections 11.2–11.3 -
36. Tue, Oct 27 Course Summary and Review — AIMA Chapters 1–11 -
37. Thu, Oct 29 Advanced Planning: A Whirlwind Tour — AIMA Sections 11.4–11.6 HW-Quiz 6
— Wed, Nov 11 End-Semester Exam (Ch. 1–11) — — —