Explore foundational AI concepts through the Pac-Man projects, designed for UC Berkeley's CS 188 course. Implement search algorithms, multi-agent strategies, and reinforcement learning techniques in Python, emphasizing real-world applications. Engage in the Eutopia Pac-Man contest for a multiplayer capture-the-flag challenge

This is a library for search algorithms that are taught in CS165A at UCSB

🔍 Implementation of Linear Search 🔍

This repository contains a comprehensive collection of Artificial Intelligence algorithms and projects, developed as part of coursework for AI in Semester 5. It showcases implementations of fundamental AI techniques and problem-solving strategies across various domains, including search algorithms, agent-based modeling, game solving.

A high-performance Rubik’s Cube solver written in modern C++. This project features multiple cube representations (3D array, 1D array, bitboard) and implements advanced solving algorithms, including BFS, DFS, IDDFS, and IDA* with pattern database heuristics. Designed for speed, modularity, and extensibility, it’s ideal for both educational purposes

A Sudoku game with an AI agent that generates puzzles, solves them, and verifies their validity using constraint satisfaction algorithms. The AI uses backtracking and heuristics for efficient solving. It also offers a web interface for users to interact with the puzzle.

Python implementation of Generate-and-Test and Hill Climbing algorithms, demonstrating core AI search techniques.