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https://github.com/nathanaelsantos/dynamic-maze-search-algorithms
Dynamic-Maze-Search-Algorithms
https://github.com/nathanaelsantos/dynamic-maze-search-algorithms
conda docker graph-tool inteligencia-artificial numpy pygame python
Last synced: 3 months ago
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Dynamic-Maze-Search-Algorithms
- Host: GitHub
- URL: https://github.com/nathanaelsantos/dynamic-maze-search-algorithms
- Owner: NathanaelSantos
- Created: 2023-02-14T23:40:21.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-16T19:09:37.000Z (6 months ago)
- Last Synced: 2024-08-16T20:30:10.455Z (6 months ago)
- Topics: conda, docker, graph-tool, inteligencia-artificial, numpy, pygame, python
- Language: Python
- Homepage:
- Size: 12.3 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Dynamic Maze with Interactive Search Algorithms Visualization
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)
https://github.com/NathanaelSantos/Dynamic-Maze-Search-Algorithms/assets/45519156/19e17396-4e67-4cb0-ae7a-c3f1397af9df
---
---
### Search Algorithm Visualizations
Q-Learning
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
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
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
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
Uniform Cost Search extends the breadth-first search to handle varying edge costs, always expanding the node with the lowest path cost.
A-star Search
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.
---
## Graphs generated based on the paths traversed by the chosen algorithm in the maze.
## Breadth-First Search Graph
## Depth-First Search Graph
## A* Search Graph
## Running AI-Maze-Game with Docker and Xming on Windows
This guide provides step-by-step instructions for running the AI-Maze-Game application using Docker and Xming on a Windows machine.
### Prerequisites
Before you begin, make sure you have the following installed:
- Docker: [Install Docker for Windows](https://docs.docker.com/docker-for-windows/install/)
- Xming: Xming-6-9-0-31-setup.exe
### Instructions
1. Install Xming by running the `Xming-6-9-0-31-setup.exe` installer.
2. Open a terminal where the Dockerfile is located.
3. Build the Docker image by running the following command: `docker build -t projetoia .`
4. 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 `projetoia` with the name of your Docker image if you used a different name in step 3.
5. Navigate to the Maze-IA directory by running the following command: `cd AI-Maze-Game/ && cd Maze-IA && ls`
6. Run the AI-Maze-Game application by running the following command: `python3 game.py`
---
## Install on Fedora
#### Install the necessary dependencies to install Miniconda
1. `sudo dnf install -y wget bzip2 ca-certificates curl glib2 libXext libSM libXrender`
#### Download the Miniconda .sh file
2. `wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh`
#### Install Miniconda in /home/youruser/miniconda3
3. `bash Miniconda3-latest-Linux-x86_64.sh -b -p /home/youruser/miniconda3`
#### add Miniconda's bin directory to your PATH:
4. `echo 'export PATH="/home/youruser/miniconda3/bin:$PATH"' >> ~/.bashrc`
#### Activate the Conda base environment by running the following command:
5. `source /home/youruser/miniconda3/bin/activate`
6. `(base) [youruser]$ conda install -y -c conda-forge graph-tool`
7. `(base) [youruser]$ conda install -y -c conda-forge pygame`
8. `(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`
---
## Installation and Execution of AI-Maze-Game on Ubuntu
## Prerequisites
Before starting, make sure you have the following prerequisites:
- Ubuntu (any recent version)
- Internet access
## Passos para Instalação
### 1. Install Necessary Dependencies
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
```
## 2. Download Miniconda
Download the Miniconda installer using the wget command.
```
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
```
## 3. Install Miniconda
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
```
## 4. Configure the PATH
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
```
## 5. Activate the Conda Environment
Activate the base Conda environment.
```
source /home/youruser/miniconda3/bin/activate
```
## 6. Install Necessary Packages
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
```
## 7. Clone the AI-Maze-Game Repository
Clone the AI-Maze-Game repository from GitHub.
```
git clone https://github.com/NathanaelSantos/AI-Maze-Game.git
```
## 8. Run the Game
Navigate to the game directory and run it.
```
cd AI-Maze-Game/
cd Maze-IA/
python3 game.py
```
# Reconnecting to the Environment
If you disconnect from the session, you can reconnect to the Conda environment by running:
```
source /home/youruser/miniconda3/bin/activate
```
---
## How to Play
- 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.
## Creators
| | | |
| :---------------------:| :------------------------:| :-----------------------:|
| 
| 
| 
|
| 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 |
### Disciplina: SINF0042 - INTELIGÊNCIA ARTIFICIAL (2022.2 - TIND01)
### Professor:
| |
| :---------------------:|
| 
| width="100" /> |
| ALCIDES XAVIER BENICASA
Departamento: Sistemas de Informação - DSI/UFS |
## License
This project is licensed under the MIT License.
Remember to replace the example information with relevant information for your project.