Artificial Intelligence

Spring Semester, 2024-25

KIIT University

Course Information

This course is an introduction to the field of Artificial Intelligence.The field AI is too wast to cover in just one course. This course provides a broad overview of AI and with more focus on classical AI rather than Machine Learning.

Syllabus

Lesson Plan

Module No. & Name Topics Video Lectures Slides
1. Introduction
  1. Introduction:- Use and Application
  2. Definition:- Thinking Humanly, Acting Humanly, Thinking Rationally and Acting Rationally
  3. Brief history of AI
 Lecture-1: Introduction to AI Slides
2. Intelligent Agents
  1. Characteristics of Intelligent Agents:- Agent Autonomy, Actuators ,Sensors, Environment, Performance Measure , Agent function and Agent Program. (Vacuum Cleaner Example, etc.)
  2. Agents and Environment:- Rational Agent , Discuss various environments, Specification of Task Environment (Using Examples).
  3. Typical Intelligent Agents and their Types:- Simple Reflex, Model based, Goal based and Utility based.
Lecture-1: Introduction to AI Slides
3. Solving Problems by Searching
  1. Defining a problem for state space searching ( State Space Representation of Water-Jug Problem, N-Queen Problem , Monks and Demons problem, 8-Puzzle problem, etc.)
  2. Search Strategies:- Search Tree, Solution Path, Nodes,Open List, Closed List, concept of space and time complexity.
  3. Uninformed Strategies:- BFS, Uniform Cost Search, DFS, Iterative Deepening, Depth, Limited, Bidirectional Search and The Space and Time complexity of each search Strategy.
  4. Informed (Heuristics Strategies):- Concept of Heuristics, Admissibility and consistency, Greedy Best First Search, A* Algorithm. Proof of Optimality of A* Search.
Lecture-2: Modeling AI Problem as a Search Problem
Lecture-3: BFS and Its Properties
Lecture-4: DFS and Its Properties
Lecture-5: DLS and IDS Search Algorithms
Lecture-6: UCS Algorithm and Its Properties
Lecture-7: Informed Search and Greedy BFS Algorithm
Lecture-8: A* Search Algorithm 		Slides - Part1 Slides - Part2
4. Local Search Algorithms
  1. Local Search Algorithms and Optimization Problems:- Objective Function, Global and Local Minimum/Maximum.
  2. Hill Climbing, Problems with Hill Climbing and Solution,Steepest Hill Climbing.
  3. Simulated Annealing
  4. Genetic Algorithm (Fitness Function, Crossover and Mutation)
Lecture-9: Local Search & Hill-Climbing Algorithm
Lecture-10: Variants of Hill-Climbing 		Slides
5. Adversarial Search
  1. Concept of Two Player Games
  2. Min-Max Algorithm
  3. Alpha-Beta Pruning
Lecture-11: Adversarial Search Slides
6. Backtracking Search
  1. Concept of Constraint Satisfaction Problems (CSPs)
  2. Formulation of a problem into CSP
  3. Crypt-Arithmetic Problem
  4. Map Coloring Problem
 Lecture-12: Constraint Satisfaction Problems Slides
7. Knowledge Representation
  1. Logical Agents:- Knowledge Base of an Agent, Wumpus World Example
  2. Entailment, Inference by Model Checking
  3. Basics of Proposilitional Logic
  4. Inference by enumeration, by resolution
  5. First Order Logic, concept of quantifiers
 To be uploaded Slides
8. Planning
  1. Planning and Search
  2. PDDL (Planning Domain Definition Language)
  3. Air Cargo Problem, Spare Tire Problem, Block World Problem
  4. Partial Order Planning
 To be uploaded Slides
9. Probabilistic Reasoning
  1. Uncertainty and Review of probability
  2. Bayesian networks
  3. Inferences in Bayesian networks
 To be uploaded Slides

Practice Exercises and Assignments

Activity Description Problem Statement / Question Paper Supplementary Resources
Quiz-1 Syllabus covered till Uninformed Search Quiz-1 Set-A
Quiz-1 Set-B
Quiz-2 Syllabus covered till Informed Search and Local Search Quiz-2 Set-A
Quiz-2 Set-B
Quiz-3 Syllabus covered till Adversarial Search and CSPs Quiz-3 Set-A
Quiz-3 Set-B
Quiz-4 Syllabus covered till Knowledge Representation Quiz-4 Set-A
Quiz-4 Set-B