AI is minimax with alpha beta pruning.
This project provides a set of functions and classes to manage the logic of a game involving two players (AI vs AI, AI vs Human, or AI vs Monte Carlo simulation). The game is divided into three stages, and the provided functions help handle the various stages and evaluate game states using heuristics.
- Print Board: Displays the current state of the game board.
- Adjacent Locations: Provides a list of neighboring positions for a given position on the board.
- Check for Player's Moves: Determines if a player can make a mill in the next move.
- Check for Mill Formation: Checks if a player has a mill on the given position.
- Count Pieces: Counts the number of pieces a player owns on the board.
- Remove Piece: Removes a piece from the board and returns updated board states.
- Possible Moves: Generates all possible moves for different stages of the game.
- Minimax Algorithm: Uses the MiniMax algorithm for decision-making in the game.
- Monte Carlo Simulation: Simulates random games for AI decision-making.
- AI vs AI: Simulates AI vs AI gameplay.
- AI vs Human: Simulates AI vs Human gameplay.
- Python 3.x
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Clone this repository:
git clone https://github.com/Bilal-Ahmad102/9_men_morris
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Navigate to the project directory:
cd 9_men_morris
- To simulate AI vs AI gameplay, you can use the
AI_VS_AI()function. - To simulate AI vs Human gameplay, use the
AI_VS_HUMAN()function. - To simulate AI vs Monte Carlo simulation, use the
min_vs_monte()function.
To run the game, use the provided functions in the code. For example, to simulate AI vs AI gameplay, execute the following:
from your_script_name import AI_VS_AI, potentialMillsHeuristic
AI_VS_AI(potentialMillsHeuristic)Replace your_script_name with the name of the Python script containing the game code.
The game board is represented as a 3x3 grid with specific positions:
0---------------------------1-----------------------------2
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| 8-------------------9---------------------10 |
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| | 16---------17----------18 | |
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3-------11--------19 20------12-------4
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| | 21-------22-----------23 | |
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| 13------------------14--------------------15 |
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5---------------------------6-----------------------------7
- The Number on interjunctions show index of agents for both player.
- when asked to enter the player select the index of interjunction for to place the agent.
- when asked to takout an agent of opponent in
AI_vs_humangame, Select the index of opponent in list of availible opponent presented in list NOT the index of opponent in board. - For example: select the opponent to takeout: [4,2,15,20], if want to select 2, then enter 1 as it is index of 2 in list. enter 3 for 20.
- printBoard: Displays the current state of the board.
- adjacentLocations: Returns a list of adjacent locations for a given position.
- isPlayer: Checks if two positions have the player on them.
- checkNextMill: Checks if a player can make a mill in the next move.
- isMill: Returns true if a player has a mill on a given position.
- numOfPieces: Returns the number of pieces owned by a player.
- removePiece: Removes a piece from the board.
- possibleMoves_stage1, possibleMoves_stage2, possibleMoves_stage3: Generate possible moves for different stages of the game.
- possibleMoves_stage2or3: Checks if the game is in stage 2 or 3 and returns possible moves.
- minimax: Implements the MiniMax algorithm for AI decision-making.
- potentialMillsHeuristic: A heuristic function to evaluate the board at the end Node (terminal Node) of min_max Tree.
- monteCarlo: Function to perform Monte Carlo simulations.
- simulateRandomGame: Simulates random games from a given board position.
- mc: Monte Carlo function to select the best move.