A dynamic maze was created to allow users to experiment with different search algorithms. The maze is randomly generated by the user, and they can choose a start and an end point. Then, they can select a search algorithm, such as breadth-first search, depth-first search, A* search, etc.
As the search progresses, the path taken is highlighted with different colors. This allows users to clearly and interactively visualize the search process.
Course: SINF0042 - ARTIFICIAL INTELLIGENCE (2022.2 - TIND01)
iavideo.mp4
Q-Learning is a reinforcement learning algorithm that learns the value of actions in a given state to maximize the total reward over time. |
Depth-First Search (DFS) explores a graph or tree by starting at the root and exploring as far as possible along each branch before backtracking. |
Breadth-First Search (BFS) traverses or searches tree or graph data structures by exploring all the neighbor nodes at the present depth prior to moving on to nodes at the next depth level. |
Greedy Best-First Search expands the most promising node according to a specified rule, aiming to reach the goal by always expanding the node that appears to be closest to the goal. |
Uniform Cost Search extends the breadth-first search to handle varying edge costs, always expanding the node with the lowest path cost. |
A-star (A*) Search combines elements of both uniform cost search and greedy best-first search to find the most efficient path to the goal using both the cost to reach the node and an estimated cost to reach the goal. |
This guide provides step-by-step instructions for running the AI-Maze-Game application using Docker and Xming on a Windows machine.
Before you begin, make sure you have the following installed:
- Docker: Install Docker for Windows
- Xming: Xming-6-9-0-31-setup.exe
- Install Xming by running the
Xming-6-9-0-31-setup.exeinstaller. - Open a terminal where the Dockerfile is located.
- Build the Docker image by running the following command:
docker build -t projetoia . - Run the Docker container with Xming by running the following command:
docker run -it --name projetoia -e DISPLAY=host.docker.internal:0 projetoia- Note: Make sure to replace
projetoiawith the name of your Docker image if you used a different name in step 3.
- Note: Make sure to replace
- Navigate to the Maze-IA directory by running the following command:
cd AI-Maze-Game/ && cd Maze-IA && ls - Run the AI-Maze-Game application by running the following command:
python3 game.py
sudo dnf install -y wget bzip2 ca-certificates curl glib2 libXext libSM libXrender
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh -b -p /home/youruser/miniconda3
echo 'export PATH="/home/youruser/miniconda3/bin:$PATH"' >> ~/.bashrc
source /home/youruser/miniconda3/bin/activate(base) [youruser]$ conda install -y -c conda-forge graph-tool(base) [youruser]$ conda install -y -c conda-forge pygame(base) [youruser]$ conda install -y numpy
make the clone: git clone https://github.com/NathanaelSantos/AI-Maze-Game.git
(base) [nathan]$ cd AI-Maze-Game/
(base) [nathan AI-Maze-Game]$ cd Maze-IA/
(base) [nathan Maze-IA]$ python3 game.py
Before starting, make sure you have the following prerequisites:
- Ubuntu (any recent version)
- Internet access
First, we need to install some necessary dependencies for the installation of Miniconda.
sudo apt-get update
sudo apt-get install -y wget bzip2 ca-certificates curl libglib2.0-0 libxext6 libsm6 libxrender1
Download the Miniconda installer using the wget command.
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
Run the Miniconda installation script and install it in the /home/yourusername/miniconda3 directory.
bash Miniconda3-latest-Linux-x86_64.sh -b -p /home/youruser/miniconda3
Add the Miniconda bin directory to your PATH so that Conda commands are recognized.
echo 'export PATH="/home/youruser/miniconda3/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc
Activate the base Conda environment.
source /home/youruser/miniconda3/bin/activate
Use Conda to install the necessary packages: graph-tool, pygame, and numpy.
conda install -y -c conda-forge graph-tool
conda install -y -c conda-forge pygame
conda install -y numpy
Clone the AI-Maze-Game repository from GitHub.
git clone https://github.com/NathanaelSantos/AI-Maze-Game.git
Navigate to the game directory and run it.
cd AI-Maze-Game/
cd Maze-IA/
python3 game.py
If you disconnect from the session, you can reconnect to the Conda environment by running:
source /home/youruser/miniconda3/bin/activate
- Press the B key to activate or deactivate the "paint mode" (activated by default).
- You can draw new walls in the maze by clicking with the left button of the mouse, while the paint mode is activated.
- Press the S key to save a maze file. You can open it in the option "Import save".
- After drawing your maze, press the SPACE key to show the search algorithm menu.
- Select the algorithm by clicking with the left button of the mouse.
- Enjoy watching.
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| Guilherme Santos Costa Bacharel em Sistemas de Informação |
José Nathanael Santos Matos Bacharel em Sistemas de Informação |
Pedro Antonio Santos Lima Bacharel em Sistemas de Informação |
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| ALCIDES XAVIER BENICASA Departamento: Sistemas de Informação - DSI/UFS |
This project is licensed under the MIT License.
Remember to replace the example information with relevant information for your project.