🧩 Mancala Game 🧩

This repo was made for the final project of our algorithms class in 2nd year CSAI. Here is a quick demo :)

MANCALA.mp4

Please note -> the main branch contains a simplified version of the UI. The extended version can be found under the Tomas branch.

Rules of the Game 📐

Game Setup:

• Two rows with six pits each.
• Player 1 (top row, CPU Player) and Player 2 (bottom row, Human Player).
• Each player has a home (leftmost and rightmost pits) which keeps track of their points.
• The game starts out with 4 stones per pit.

Objective:

• Capture more stones than your opponent.
• Game ends when all pits on one side are empty.

Gameplay:

Starting the Game:

• Players take turns;
• Player 2 (so the human player) starts.

Turn Mechanics:

• Select a pit, pick up its stones, and distribute counterclockwise. This is all simulated in one click.
• If the last stone lands in the player's home, they get another turn.

Capturing Stones:

• If the last stone lands in an empty pit on the player's side, and the opposite pit on the opponent's side has stones, capture all those stones.

End Game:

• Game ends when one player's pits are empty.
• Remaining stones on the opponent's side are captured.

Determining the Winner:

• Count seeds in the store; player with the most seeds wins.

Extra functionalities:

• Press l to view the leaderboard
• Press q to quit the game
• Press r to restart

Key Data Structures ✔️

Matrices:

• Represent the game board; used to store the visual/image representation of the number of stones in each pit.
• Accessed and modified to visually simulate stone movement.

Dictionary (board_update method):

• Serves to easily simulate the counterclockwise movement of mancala.
• Helps the visual board representation by translating board_dictionary to respective pits and scores in the matrix.
• Manages the logical state of the board independently of the visual interface.

Tree (AI player)

• Dynamically created to evaluate each possible move
• Serves to visualise the possible upcoming moves
• This helps the AI evaluate what move would lead to the best outcome

Key Algorithms ✔️

DFS sum

• Used to transverse the tree of possible moves
• It adds the score by the human player (as a loss) and the AI (as a gain) to check what combination would be best.
• The entire implementation of the AI player includes a depth parameter that can be adjusted to make the AI think more turns ahead.

Merge sort

• Used to sort the leaderboard kept in leaderboard.txt
• This algorithm was chosen because of its time complexity characteristics reaching at worst 0(nlogn)