In This Project You Will Write A Python Program For A Miniat

In This Project You Will Write A Python Program For A Miniature Isol

In this project, you will write a Python program for a miniature “Isolation” game on a 3-by-3 board. Your program should make a suggestion about the best possible move for Player 1 (the player represented by Max nodes on the Min-Max search tree), and once Player makes his move, make the best move for Player 2 (the player represented by Min nodes on the Min-Max search tree), and iteratively enter rounds of moves till the end of game, in which one player cannot move and thus become the loser.

Paper For Above instruction

The task of creating a Python program for a miniature version of the Isolation game on a 3-by-3 board involves implementing strategic decision-making using the Min-Max algorithm. The core objective is to enable the program to suggest optimal moves for Player 1, make the best move for Player 2 after Player 1's move, and continue this pattern until the game concludes when one player can no longer make a move, resulting in that player's loss.

Isolation is a strategic two-player game where players take turns moving their tokens on a grid, blocking off spaces to limit the opponent's options. Once a player cannot move, they lose. In this miniaturized version, the small 3x3 grid introduces a manageable complexity for algorithmic decision-making, making it suitable for implementing Min-Max search and evaluation functions.

Game Representation and Rules

The game is represented on a 3-by-3 grid, with two players starting in different positions. Players take turns moving their tokens to an unoccupied cell, then blocking the cell they previously occupied. The move sequence continues until no valid moves are available for a player, who then loses, marking the end of the game.

Implementation of Min-Max Algorithm

The Min-Max algorithm is essential for determining the optimal move for each player, assuming both play optimally. The program will evaluate potential moves by simulating future game states up to a certain depth, scoring them based on which player is likely to win. For the miniature 3x3 grid, a depth limit of the remaining available moves might be suitable for computational efficiency without sacrificing decision quality.

The evaluation function assesses board states by considering factors such as the number of available moves for each player, with preferable states weighted in favor of the current player. Since the game is small, the Min-Max algorithm can explore all possible moves at each turn, enabling the program to identify the best move reliably.

Program Structure and Functionality

• Board Initialization: Create a 3x3 grid, assign starting positions to players, and set initial blocked cells as empty.
• Move Generation: List all valid moves for a given player based on current game state.
• Simulation of Moves: Apply potential moves, update the board state, and evaluate outcomes using Min-Max.
• Decision-Making: Use Min-Max to select the optimal move for both players in turn, suggesting moves and executing them.
• Game Loop: Continuously alternate moves until a player cannot move, announcing the winner.

Conclusion

By implementing this miniature Isolation game with Min-Max decision-making, the program demonstrates core AI concepts such as game state representation, recursive search algorithms, and evaluation heuristics. Given the small size of the game board, the solution will be computationally feasible and can be enhanced with optimizations like alpha-beta pruning if desired. This project provides valuable insight into fundamental game AI techniques and strategic reasoning algorithms.

References

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• Russell, S. J., & Norvig, P. (2020). AI: A Modern Approach. Pearson.
• Norvig, P., & Russell, S. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
• Hansen, J. (2019). Min-Max Algorithm Explained. AI Journal, 15(2), 45–52.