AI-Search

Check this app at: https://ali-elganzory.github.io/AI-Search/

Description

An educational app for visualizing the different searching algorithms in the field of artificial intelligence by offering

Undirected / Directed graph construction.
Edge weights and state heuristics assignment.
Multiple search algorithm choices.

Supported Searching Algorithms

Breadth First Search
Depth First Search
Depth Limit Search
Iterative Deepening Search
Uniform Cost Search
Greedy Search
A Star Search

App Tech Stack

The app is built using HTML and Python as a website that can be run on any browser. To use the Web API, a Python-JavaScript transcompiler called Brython is used.

Source Code

The app source code is attached with this document. Also, the app is source controlled by git and is available at this repository. You can browse the incremental phases we followed to develop the app.

How to Run

Method 1

The app is deployed on GitHub Pages; Open this link AI Search (ali-elganzory.github.io).

Method 2

1 – Run the command [python -m http.server] in the root directory of the project to get an http server up and running – ready to serve the website.

2 – Open http://localhost:8000/index.html.

Method 3

1 – Open the root directory of the project in Visual Studio Code.

2 – Install "Live Server" vscode extension.

3 – Click "Go live" at the right bottom of the editor; It will open the app website in your browser automatically.

Use Instructions – Demo

1 – Open the app. The tools are labeled on the below figure.

2 – Start constructing the graph. You can

Add a node by clicking where you want it to be drawn.
Add an edge between two nodes A and B by clicking on A then B.
Set / Remove a goal by clicking on any node – except the source.
Set weights and heuristics by clicking on the edge weight text and nodes respectively.

An example graph is in the below figure.

3 – Choose a searching algorithm from the bottom and click solve. The above graph is modified (goals, weights, and heuristics) and solved using A\* is shown below.

The agent starts to paint the visited nodes purple , and then, when it finds a goal, it paints the solution path with orange as seen in the above figure.

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