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Bot solving Nonogram puzzles using search problem models

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Searching Case Study: Nonogram

How to use the GUI

Start the GUI app by running gui.py:

python gui.py
Button Feature
Generate Generate new puzzle (not ready, restart the app to get a new one :D)
Solve Solve the puzzle
Run Automatically solve and run the solution step by step
Previous Step Navigate to the previous step of the solution
Next Step Navigate to the next step of the solution
Skip Skip all the steps and see the solution (quickly :)
DFS/BeFS Switch the search algorithm

The default size of board is 5x5. You can customize it by modifying the following line:

NONOGRAM_BOARD_SIZE = 5

We do not recommend playing with any board larger than 7x7 :)

Performance Testing

Run main.py to check out the benchmarks (time and memory) of DFS (Depth First Search) and BeFS (Best First Search)

python main.py

There are six testcases for 5x5, 6x6 and 7x7 boards. After running the above command, the terminal will show the time and memory usage by DFS and BeFS. You can check out the files in output to see each step.

Acknowledgements

We are highly inspired by the AIMA books and the AIMA code implementation using Python by Russell and Norvig.

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