֎ AI Algorithms Guide

This repository contains a collection of Artificial Intelligence algorithms implemented in Python, organized by "Tasks". These implementations cover a wide range of topics including Search Strategies, Constraint Satisfaction Problems (CSPs), Adversarial Search, Logic, and Probabilistic Reasoning (Bayesian Networks).

☰ Table of Contents

• Dependencies
• Tasks Overview
• Usage

🖇️ Dependencies

The project relies on the following Python packages. You can install them using pip:

pip install numpy pandas scipy networkx tabulate psutil

🗹 Tasks Overview

The codebase is structured into numbered Task folders, each focusing on specific AI concepts:

Task	Topic	Algorithms / Description	Key Files
Task 1	Graph Search	BFS, DFS, UCS, IDS (Iterative Deepening Search). Implementation of standard uninformed and informed search algorithms on dense graphs.	TASK 1/
Task 2	Heuristic Search	A*, RBFS (Recursive Best-First Search). Solving the 8-Puzzle problem using Manhattan distance heuristic.	TASK 2/
Task 3	Local Search	Hill Climbing (with Random Restarts). Applied to 8-Queens, 8-Puzzle, and TSP (Traveling Salesperson Problem).	TASK 3/
Task 4	CSP	Backtracking Search for Graph Coloring. Includes MRV (Minimum Remaining Values), LCV (Least Constraining Value) heuristics, and Forward Checking.	TASK 4/
Task 5	Adv. Search / CSP	Sudoku Solver. Compares Backjumping vs. Backjumping with Heuristics (MRV + LCV).	TASK 5/
Task 6	Logic	Propositional Logic Resolution. A theorem prover using CNF conversion and resolution refutation.	TASK 6/
Task 7	Adversarial Search	Tic-Tac-Toe AI. Implements Minimax and Alpha-Beta Pruning.	TASK 7/
Task 8	Logic	First-Order Logic. Implements Forward Chaining with unification and substitutions.	TASK 8/
Task 9	Bayesian Networks	Exact Inference. Enumeration algorithm for answering queries on a Bayes Net (Burglary/Alarm example).	TASK 9/
Task 10	Bayesian Networks	Approximate Inference. Sampling methods: Prior Sampling, Rejection Sampling, Likelihood Weighting, Gibbs Sampling.	TASK 10/

⚙ Usage

Each lab task can be run independently. Navigate to the specific directory or run the scripts from the root.

Example: Running the 8-Puzzle Solver (Task 2)

python "TASK 2/task_2.1.py"

Example: Running the Tic-Tac-Toe AI (Task 7)

python "TASK 7/task_7.py"

Follow the on-screen prompts to choose between Minimax or Alpha-Beta.

"Guide created by Soham Tripathy 👨🏻‍💻"

for contact mail 423194@student.nitandhra.ac.in