https://github.com/abd-mohsen/xlox

XLOX board game, can be solved by user or by computer using state space search algorithms
https://github.com/abd-mohsen/xlox

ai algorithms-and-data-structures state-space-search

Last synced: 13 days ago
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XLOX board game, can be solved by user or by computer using state space search algorithms

• Host: GitHub
• URL: https://github.com/abd-mohsen/xlox
• Owner: abd-Mohsen
• Created: 2023-11-10T21:36:58.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2024-01-21T14:15:03.000Z (12 months ago)
• Last Synced: 2024-11-07T04:44:33.396Z (2 months ago)
• Topics: ai, algorithms-and-data-structures, state-space-search
• Language: Dart
• Homepage: https://xlox-abdmoh.000webhostapp.com/
• Size: 345 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# XLOX

a board game called XLOX, developed using Flutter framework and Dart language.

inspired from [here](https://www.puzzleplayground.com/xlox).

## Game Description

XLOX is a one-player board game where the objective is to get rid of all white cells in a given grid. can be played manually or by letting the 

computer solve it for us.

### Game Rules

- There is three types of cells, empty, white and a wall

- you can click only on white cells.

- when you click on a white cell, all cells adjacent to it (top, bottom, left and right cells) will flip (empty to white and white to empty).

- wall cells remain intact.

## Computer AI

implemented several state space search algorithms (informed and uninformed) in order to reach final state for a given grid.

- **Depth First search (DFS) :** we use a stack or a recursive method to generate all possible next state for the current state and traverse them.

- **Breadth First search (BFS) :** we use a queue or to generate all possible next state for the current state and traverse them.

- **Uniform Cost Search (UCS) :** similar to bfs , but we use a min heap (priority queue) to sort the states efficiently based on their cost.

- **Hill climbing :** similar to bfs , but we use a min heap (priority queue) to sort the states efficiently based on their heuristic value.

- **A\* (A star) :** similar to bfs , but we use a min heap (priority queue) to sort the states efficiently based on their heuristic value + cost.

## Results

- some levels are impossible to solve using uninformed search algorithms.

- using a good heuristic value can make a huge difference.

##

This game was developed as an assignment for a course at Damascus university. The assignment is aimed to demonstrate proficiency in implementing 

search algorithms on a real world problem.

Feel free to explore the source code and contribute to the project if you'd like!