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An Artificial Intelligence Project based on UC Berkeley's Pacman AI course

Home Page: http://ai.berkeley.edu/project_overview.html

Python 99.97% Makefile 0.03%
adversarial-learning astar-search-algorithm breadth-first-search constraint-satisfaction-problem depth-first-search maintaining-arc-consistency reinforcement-learning uniform-cost-search

aiproject-pacman's Introduction

An Artificial Intelligence Project

This project is a solution for the UC Berkeley Pacman AI course project, incorporating additional code and solutions. The project covers various topics in artificial intelligence, including Uninformed and Informed Search, Game Search, Constraint Satisfaction Problem (CSP), Probabilistic Inference using Bayesian Networks, and Reinforcement Learning.

Table of Contents

Introduction

This project provides a comprehensive solution for the UC Berkeley Pacman AI course project. It builds upon the existing codebase provided by the course, incorporating additional code and solutions to complete the various assignments, beyond the Pacman project as well.

The project covers several fundamental concepts in artificial intelligence, allowing exploration and implementation of algorithms related to Uninformed and Informed Search, Game Search, Constraint Satisfaction Problem (CSP), Probabilistic Inference using Bayesian Networks, and Reinforcement Learning.

Project Structure

  • search/ - Contains code related to search algorithms, including uninformed and informed search.
  • multiagent/ - Contains code for multi-agent search algorithms, which are used in the game-playing scenarios.
  • reinforcement/ - Contains code that implements reinforcement learning algorithms to train Pacman agents, allowing them to learn and improve their behavior through interaction with the environment.
  • tracking/ - Contains code dealing with probabilistic inference in a hidden Markov model tracks the movement of hidden ghosts in the Pacman world.
  • csp/ - Separate from the Pacman game, this contains code for dealing with Constraint Satisfaction Problems (CSPs) like Kakuro.

Installation

To get started with the project, follow these steps:

  1. Clone the repository:
    git clone https://github.com/aravindsomaraj/aiproject-pacman.git
    
  2. Python3 is required. No other dependencies required.
    python3 --version
    
    # if not installed
    sudo apt-get install python3
    

Usage

  1. Navigate to the project directory:
    cd aiproject-pacman
    
    Specific instructions for each sub-directory is mentioned further in.

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