Foundations of Artificial Intelligence-CUAI1001

Teacher

Mr.Debasish Bhola

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Course Objective:

  • Understand fundamental concepts of Artificial Intelligence
  • - Explore different AI search techniques and intelligent agent architecture
  • - Study logical reasoning, knowledge representation, and inference
  • - Gain exposure to game theory and expert system design

- Implement basic AI algorithms and applications in Python

Course Outcomes:

After completion of the course students will be able to:

CO1. Understand the scope and history of Artificial Intelligence and its real-world applications

CO2. Formulate AI problems and solve them using appropriate search strategies

CO3. Analyze and design intelligent agents and their behavior

CO4. Apply logical reasoning for knowledge representation and inference

CO5. Develop simple expert systems and AI-based applications

Course Contents

Module 1: Introduction to AI (4 hours)

- Definition and goals of AI

- History and evolution

- Types of AI: Narrow, General, Super

- Applications in various domains

- Ethical considerations in AI

Module 2: Intelligent Agents (4 hours)

- Agents and environments

- PEAS (Performance, Environment, Actuators, Sensors)

- Agent types: simple reflex, model-based

- Structure of intelligent agents

- Rationality and autonomy

Module 3: Uninformed Search Techniques (8 hours)

- Problem formulation

- Breadth-first search (BFS)

- Depth-first search (DFS)

- Uniform cost search

- Depth-limited and iterative deepening search

Module 4: Informed Search and Heuristics (8 hours)

- Best-first search

- A* algorithm

- Heuristic functions and their properties

- Hill climbing

- Simulated annealing and local search

Module 5: Game Playing (6 hours)

- Adversarial search concepts

- Minimax algorithm

- Alpha-beta pruning

- Evaluation functions

- Game trees and cutoffs

Module 6: Knowledge Representation (8 hours)

- Propositional logic

- First-order predicate logic

- Unification and resolution

- Inference mechanisms

- Forward and backward chaining

Module 7: Expert Systems and Applications (6 hours)

- Rule-based systems

- Architecture of expert systems

- Case studies: MYCIN, DENDRAL

- Agent architectures

- Chatbots and AI applications

Practicals:

- Implement BFS and DFS in Python for maze problems

- Apply A* search on heuristic grid path planning

- Simulate tic-tac-toe using minimax with alpha-beta pruning

- Develop a rule-based medical diagnosis system

- Model a grid-world intelligent agent using PEAS representation

- Create a chatbot with hardcoded logical responses

- Build a simple decision-making system using predicate logic

Recommended Textbooks:

1.Stuart Russell and Peter Norvig, "Artificial Intelligence: A Modern Approach", 4th Edition, Pearson

2.Elaine Rich, Kevin Knight, and Shivashankar B. Nair, "Artificial Intelligence", McGraw-Hill

3.Ivan Bratko, "Prolog Programming for Artificial Intelligence", Pearson

SESSION PLAN

SESSION-2

Project Session

SESSION-3

Python enviorment setup

Video-https://www.youtube.com/watch?v=C3bOxcILGu4

SESSION-4

Python Basic Program

1.Print Hello World

2.Swap Two Numbers

3.Check Even or Odd

5.Find the Factorial of a Number

SESSION -5

Project Session

SESSION-8

Program Like Usecase of libray  of Python

SESSION-9

Project Session

SESSION-10

Project Session

SESSION-12

Project Session

SESSION-13

Python Program Pratice

SESSION-14

Python Program Pratice using VS Code

SESSION-15

Project Session

SESSION-17

Project Session

SESSION-18

Python Program Pratice using VS Code

SESSION-19

Python Program Pratice using VS Code

SESSION-20

Project Session

SESSION-21

Agent types: simple reflex, model-based

Video-https://www.youtube.com/watch?v=QKXk9SowwT4

 

Structure of intelligent agents

https://www.slideshare.net/slideshow/structure-of-agents/222323196

 

Video-https://www.youtube.com/watch?v=FC0fAC0Z-x4

SESSION-22

Project Session

SESSION-23

Python Program Pratice using VS Code

SESSION-24

Python Program Pratice using VS Code

SESSION-25

Project Session

SESSION-27

Project Session

SESSION-28

BFS Programming Using Python

1.Write a Python program to perform BFS traversal on a graph represented using an adjacency list.

2.Find the shortest path in a binary matrix (0s = blocked, 1s = path) from source to destination using BFS.

SESSION-29

Problem Solving  Program Using Python

1.Find the Largest of Three Numbers
Use conditional statements to solve.

2.rint Fibonacci Series up to n Terms
Solve using iteration and recursion.

SESSION-30

Project Session

SESSION-32

Project Session

SESSION-33

DFS Programming using Python

SESSION-34

DFS Programming using Python

SESSION-35

Project Session

SESSION-37

Project Session

SESSION-38

Uniform cost search Programming

SESSION-39

Cost Search Programming

SESSION-40

Project Session

SESSION-42

Project Session

SESSION-43

Best First Search Programming

SESSION-44

A* Algorithim

  1. Shortest Path in a Grid (2D Maze Problem)
  2. A* Pathfinding on a Weighted Graph

Word Ladder Solver (Heuristic: Edit Distance)

SESSION-45

Project Session

SESSION-47

Project Session

SESSION-49

local search

Place N queens on an N×N chessboard such that no two queens attack each other.

-Use hill climbing or simulated annealing.

-nitial state: Random positions of queens.

Heuristic: Number of attacking pairs

SESSION-50

Project Session

SESSION-52

Project Session

SESSION-53

Game trees and cutoffs Programming

  • Implement Minimax Algorithm for Tic-Tac-Toe
  • Implement Game Tree for Nim Game

AI Bot vs Human with Limited Depth

SESSION-54

Alpha-beta pruning Programming

1-Implement Alpha-Beta Pruning for a Binary Tree

2-Maximize Score in a 2-Player Stone Game

SESSION-55

Project Session

SESSION-57

Project Session

SESSION-58

First-order predicate logic Programming

  • Implement a unification algorithm in Python to check if two logical predicates can be unified.
  • Define logical rules to represent family trees.

3-Take simple English sentences and convert them to FOPL programmatically.

SESSION-59

Forward and backward chaining Programming

SESSION-60

Project Session

SESSION-62

Project Session

SESSION-63

Create a chatbot with hardcoded logical responses

SESSION-64

Build a simple decision-making system using predicate logic

SESSION-65

Project Session

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