# Mines Finder AI
## Overview
The Mines Finder AI is designed to analyze and solve a Minesweeper-style game on a 5x5 grid with a variable number of hidden mines. The AI will identify potential mine locations based on logical deductions, adapt its strategy dynamically as cells are revealed, and provide a user-friendly interface with visual feedback and explanations for its decisions.
## Technical Requirements
* Input: A 5x5 grid with revealed numbers or empty cells and the total number of mines.
* Output: A marked grid highlighting flagged mines and cells to be safely revealed.
## Implementation
### Grid Representation
The grid will be represented as a 5x5 matrix, where each cell can have one of the following values:
* `0`: Empty cell
* `1-8`: Number of adjacent mines
* `M`: Mine
* `F`: Flagged mine
### Algorithm
The AI will use a combination of logical deductions and probabilistic reasoning to identify potential mine locations.
1. **Initialization**: The AI will start by analyzing the revealed cells and identifying potential mine locations based on the numbers and their adjacent cells.
2. **Deduction**: The AI will apply logical deductions to identify cells that are guaranteed to be mines or safe to reveal.
3. **Probabilistic Reasoning**: The AI will use probabilistic reasoning to identify cells that are likely to be mines or safe to reveal.
4. **Flagging**: The AI will flag cells that are likely to be mines.
5. **Revealing**: The AI will reveal cells that are safe to reveal.
### User Interface
The AI will provide a user-friendly interface with visual feedback and explanations for its decisions.
* **Grid Display**: The AI will display the grid with flagged mines and cells to be safely revealed.
* **Decision Explanation**: The AI will provide explanations for its decisions, including the logical deductions and probabilistic reasoning used.
## Code Implementation
```python
import numpy as np
class MinesFinder:
def __init__(self, grid, num_mines):
self.grid = grid
self.num_mines = num_mines
self.flagged_mines = []
def analyze_grid(self):
# Analyze the grid and identify potential mine locations
for i in range(5):
for j in range(5):
if self.grid[i, j] == 0:
# Check adjacent cells
adjacent_cells = self.get_adjacent_cells(i, j)
for cell in adjacent_cells:
if self.grid[cell[0], cell[1]] != 0:
# Update the cell value based on the number of adjacent mines
self.grid[i, j] = self.grid[cell[0], cell[1]]
def get_adjacent_cells(self, i, j):
# Get the adjacent cells of a given cell
adjacent_cells = []
for x in range(-1, 2):
for y in range(-1, 2):
if x == 0 and y == 0:
continue
if i + x >= 0 and i + x < 5 and j + y >= 0 and j + y < 5:
adjacent_cells.append((i + x, j + y))
return adjacent_cells
def flag_mines(self):
# Flag cells that are likely to be mines
for i in range(5):
for j in range(5):
if self.grid[i, j] != 0 and self.grid[i, j] == self.num_mines:
self.flagged_mines.append((i, j))
self.grid[i, j] = 'F'
def reveal_cells(self):
# Reveal cells that are safe to reveal
for i in range(5):
for j in range(5):
if self.grid[i, j] == 0:
# Check if the cell is safe to reveal
if self.is_safe_to_reveal(i, j):
self.grid[i, j] = 'R'
def is_safe_to_reveal(self, i, j):
# Check if a cell is safe to reveal
adjacent_cells = self.get_adjacent_cells(i, j)
for cell in adjacent_cells:
if self.grid[cell[0], cell[1]] == 'F':
return False
return True
def display_grid(self):
# Display the grid with flagged mines and cells to be safely revealed
for i in range(5):
for j in range(5):
if self.grid[i, j] == 'F':
print('F', end=' ')
elif self.grid[i, j] == 'R':
print('R', end=' ')
else:
print(self.grid[i, j], end=' ')
print()
# Example usage
grid = np.zeros((5, 5), dtype=int)
grid[0, 0] = 1
grid[0, 1] = 2
grid[1, 0] = 3
num_mines = 5
mines_finder = MinesFinder(grid, num_mines)
mines_finder.analyze_grid()
mines_finder.flag_mines()
mines_finder.reveal_cells()
mines_finder.display_grid()
```
## Optimization
The AI will optimize for both accuracy and speed by:
* Using a combination of logical deductions and probabilistic reasoning to identify potential mine locations.
* Adapting its strategy dynamically as cells are revealed.
* Using a efficient algorithm to analyze the grid and identify potential mine locations.
## Robustness
The AI will ensure robustness for edge cases, such as minimal or near-full mine coverage, by:
* Handling cases where the number of mines is close to the total number of cells.
* Using probabilistic reasoning to identify cells that are likely to be mines or safe to reveal.
## Goals
The AI will minimize user interaction for efficient solving by:
* Automating the analysis and flagging of potential mine locations.
* Providing clear explanations for its decisions.
The AI will explain decisions clearly to improve player understanding of the game by:
* Providing a user-friendly interface with visual feedback and explanations for its decisions.
* Displaying the grid with flagged mines and cells to be safely revealed.
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