CS304 Artificial Intelligence

• Course Structure: 3-1-0-0-4

• This course introduces the theoretical and computational techniques of artificial intelligence.

• Topics to be covered include: Introduction of AI, Agent and environment, Problem solving by search, Knowledge representation and reasoning, Planning, Learning.

Module 1: Introduction

Definition and history of AI, Introduction to Intelligent agents: PEAS Description, Agent types, Environment.

Module 2: Problem Solving

Uninformed search, Breadth First Search, Depth First Search, Depth Limiting Search, Iterative Deepening Search, Informed search, Greedy Best First Search, A* Search and its admissibility.

Module 3: Beyond Classical Search

Local search: Hill Climbing, Simulated Annealing, Game tree and Adversarial Search: Min-Max Algorithm, Alpha-Beta Pruning, Constraint Satisfaction Problems: CSP, Solving CSP, Arc Consistency and AC-3.

Module 4: Logical Agents & Propositional Logic

Propositional logic, Propositional inference mechanisms: Proof by deduction, Conjunctive normal form, First Order Logic, Resolution, Forward chaining and Backward Chaining.

Module 5: Planning

Classical planning, Planning graphs, Hierarchical planning.

Module 6: Learning

Bayesian Learning, Introduction to Supervised Learning, Unsupervised Learning and Reinforcement.

Week 1:

• Lecture 1: Introduction; Source: Chapter 1, Russell Norvig

• Tutorial 1: State Space Search; Source: Chapter 3, Russell Norvig

• Lecture 2: Automated Problem Solving; Source: Chapter 2, Russell Norvig

• Lecture 3: Agents and Environment; Source: Chapter 2, Russell Norvig

Week 2:

• Lecture 4: Uninformed Search 1; Source: Chapter 3, Russell Norvig

• Tutorial 2: Production System; Source: Chapter 3, Russell Norvig

• Lecture 5: Uninformed Search 2 ; Source: Chapter 3, Russell Norvig

• Lecture 6: Informed Search 1; Source: Chapter 3, Russell Norvig

Week 3:

• Lecture 7: Informed Search 2; Source: Chapter 3, Russell Norvig

• Tutorial 3: Search (Problem Solving); Source: Chapter 3, Russell Norvig

• Lecture 8: Informed Search 3; Source: Chapter 3, Russell Norvig

Week 4:

• Lecture 9: Informed Search 4; Source: Chapter 3, Russell Norvig

• Tutorial 4: Quiz 1

• Lecture 10: Game Search 1; Source: Chapter 5, Russell Norvig

Week 5:

• Lecture 11: Game Search 2; Source: Chapter 4, Russell Norvig 

• Tutorial 5: Quiz 1 (Problem Solving); Source: Chapter 5, Russell Norvig

• Lecture 12: Local Search 1; Source: Chapter 4, Russell Norvig

Week 6:

• Lecture 13: Local Search 2; Source: Chapter 4, Russell Norvig

• Lecture 14: Genetic Algorithms; Source: Chapter 4, Russell Norvig

• Lecture 15: Constraint Satisfaction Problem 1; Source: Chapter 6, Russell Norvig

Week 7:

• Lecture 16: Constraint Satisfaction Problem 2; Source: Chapter 6, Russell Norvig

• Tutorial 6: Constraint Satisfaction Problem (Problem Solving); Source: Chapter 6, Russell Norvig

Week 8:

• Midsem

Week 9:

• Lecture 17: Constraint Satisfaction Problem 3; Source: Chapter 6, Russell Norvig

• Lecture 18: Logical and Knowledge-based Agents and Propositional Logic 1; Source: Chapter 7, Russell Norvig

• Lecture 19: Propositional Logic 2; Source: Chapter 7, Russell Norvig

• Lecture 20: First-Order Predicate Logic; Source: Chapter 8, Russell Norvig

Week 10:

• Lecture 21: Inference in First-Order Logic 1; Source: Chapter 9, Russell Norvig

• Tutorial 7: Propositional and Predicate Logic (Problem Solving); Source: Chapter 7, 8, & 9, Russell Norvig

• Lecture 22: Inference in First-Order Logic 2; Source: Chapter 9, Russell Norvig

• Lecture 23: Planning; Source: Chapter 10, Russell Norvig

Week 11:

• Lecture 24: Planning Graphs; Source: Chapter 10, Russell Norvig

• Tutorial 8: Planning (Problem Solving); Source: Chapter 10, Russell Norvig

• Lecture 25: Hierarchical and Multi-agent Planning; Source: Chapter 11, Russell Norvig

• Lecture 26: Knowledge Representation; Source: Chapter 12, Russell Norvig

Week 12:

• Lecture 27: Acting under Uncertainty 1; Source: Chapter 13, Russell Norvig

• Tutorial 9:  Planning 2 (Problem Solving); Source: Chapter 10, Russell Norvig

• Lecture 28: Acting under Uncertainty 2; Source: Chapter 13, Russell Norvig

• Lecture 29: Acting under Uncertainty 3; Source: Chapter 13, Russell Norvig

Week 13:

• Lecture 30: Forms of Learning; Source: Chapter 14, Russell Norvig

• Tutorial 10: Probabilistic Reasoning (Probelm Solving); Source: Chapter 14, Russell Norvig

• Lecture 31: Decision Tree; Source: Chapter 18, Russell Norvig 

• Lecture 32: Neural Networking; Source: Chapter 18, Russell Norvig

Week 14:

• Doubt-clearing classes

Week 15:

• Endsem

Stuart Russell, Peter Norvig, Artificial intelligence : A Modern Approach, Prentice Hall, Fourth edition, 2020

Assignment 1 (10%), Quiz 1 (10%), Midterm Exam (30%), Assignment 2 (10%), Quiz 2 (10%); Endterm Exam (30%)