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Project 1 - Search/
Project 2 - Multi-Agent Search/
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# AI-Pacman-Explorer Welcome to the AI Pacman Explorer repository! This collection represents my journey through the AI course, CS 383, undertaken last semester at the University of Massachusetts Amherst. These projects encapsulate an exploration of various AI techniques applied to the classic game Pacman, showcasing the evolution of intelligent agents through different challenges. ## Overview Embark on a captivating AI adventure with Pacman! This repository encompasses a series of projects that gradually introduce fundamental and advanced concepts in artificial intelligence, offering hands-on experience in creating intelligent agents for Pacman. ## Projects Included: ## Project 1: Search Algorithms Implement and master search algorithms guiding Pacman through mazes. Solve problems using uninformed and informed search strategies, establishing the groundwork for subsequent projects. ## Project 2: Multi-Agent Search Elevate Pacman's skills by introducing agents that consider the presence of ghosts. Implement adversarial search techniques, such as minimax, alpha-beta pruning, and expectimax, to navigate complex scenarios. ## Project 3: Reinforcement Learning Dive into reinforcement learning, implementing value iteration and Q-learning. Apply these techniques to scenarios like Gridworld, enabling Pacman to learn optimal policies for dynamic environments. ## Project 4: Ghostbusters Equip Pacman with sensors and delve into Bayesian networks to track invisible ghosts. Implement exact and approximate inference techniques, combining prior knowledge and real-time observations for efficient ghost hunting. Joint Particle Filter Enhance ghost tracking with a joint particle filter, allowing Pacman to consider dependencies between ghost movements. Implement observe-update and time-elapse procedures for accurate tracking in dynamic scenarios. ## Getting Started: - Clone the repository. - Navigate to the project directory you want to explore. - Follow project-specific instructions in each project's README file. ## Contributions: Contributions are welcome! Submit pull requests for improvements or additional features. ## Acknowledgments: Inspired by the AI course curriculum at UMass Amherst (CS 383), these projects owe gratitude to the educational community, instructors, and contributors who make these materials accessible. Happy coding and exploring with AI Pacman!

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