AI-class

This repository contains a collection of projects and algorithms developed as part of an Artificial Intelligence (AI) course. The focus spans multiple AI paradigms, including search algorithms, constraint satisfaction problems, and game-playing strategies, implemented across multiple programming languages (Java, C#, Python).

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📁 Repository Structure

1. CSP-Graph-Colouring/

• Language: Java
• Description: This directory contains an implementation of the Graph Colouring problem modeled as a Constraint Satisfaction Problem (CSP). The goal is to assign colours to the vertices of a graph such that no two adjacent vertices share the same colour, while using the minimum number of colours possible.
• Highlights:
  • Representation of graphs using adjacency structures.
  • Backtracking search with constraint propagation (e.g., forward checking).
  • Heuristics such as Minimum Remaining Values (MRV) and Degree Heuristic.

2. MiniMax_Tic_Tac_Toe/

• Language: C#
• Description: Demonstrates the Minimax algorithm in a classic Tic-Tac-Toe game setting. The uploaded files include the core game logic and AI decision-making code.
• Note: Although the application was designed with a GUI, only the core .cs files are included in the repository. These files are sufficient to understand the AI logic behind move selection and game-tree traversal.
• Highlights:
  • Minimax algorithm with depth-based evaluation.
  • Game state representation and move generation.
  • Turn-based logic and endgame evaluation.

3. Standalone Python Scripts

These scripts cover fundamental search algorithms and local search methods, commonly taught in AI courses:

File Name	Description
best_first_search.py	Implements the Best-First Search strategy using a heuristic function.
breadth_first_search.py	Standard Breadth-First Search (BFS) implementation using a FIFO queue.
depth_first_search.py	Implements Depth-First Search (DFS) using a LIFO stack.
priority_queue.py	A custom priority queue class used in best_first_search.py.
queue.py	Contains FIFOQueue and LIFOQueue classes used for BFS and DFS.
min_conflicts_ls_8queens.py	Solves the 8-Queens problem using Min-Conflicts, a local search heuristic.
simulated_annealing_8queens.py	Solves the 8-Queens problem using Simulated Annealing.

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🧠 Algorithms Overview

Search Techniques

• Breadth-First Search (BFS): Explores all nodes at the current depth before moving to the next level.
• Depth-First Search (DFS): Explores as far as possible along a branch before backtracking.
• Best-First Search: Uses a priority queue based on a heuristic evaluation of node desirability.

Local Search

• Min-Conflicts (8 Queens): A heuristic repair method that starts with a complete assignment and iteratively minimizes constraint violations.
• Simulated Annealing (8 Queens): A probabilistic technique to approximate the global optimum of a function in a large search space.

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🚀 Getting Started

Clone the repository:

git clone https://github.com/your-username/AI-class.git
cd AI-class