Minesweeper Classic 2 vs Minesweeper ~

Minesweeper 2 is the sequel to the classic retro PC game, bringing the nostalgic game you know and love to your mobile phone. The game is simple: use strategy and logic to avoid the bombs to clear the minefield. Challenge yourself and use the custom board builder to create your own puzzles. Track progress and game stats to beat your own times & scores. If you like old classic games like chess, checkers, backgammon, solitaire, freecell, and classic puzzle games like sudoku, tetris, and crossword puzzles then this game is for you. How to Play: Start with a covered mine field of square tiles and clear the field by tapping one square at a time. Beneath each tile is is either a number to indicates how many mines are touching the tile, a blank space, or a mine that will explode when tapped. Use strategy and logic to navigate the mine map and win the game! Features: High Score leaderboard for each difficulty setting Stats tracking to keep monitor your progress Select level of difficulty: Beginner, Medium, Difficult Customize your game for an even greater challenge Flag Mode: Mark tiles you think are bombs Easy to use interface, smooth gaming experience Pinch to zoom Use hints to reveal the location of a single bomb Portrait & Landscape mode available on both iPhone and iPad Thanks for playing Minesweeper Classic 2! Have questions or feedback? Email us at contact@maplemedia.io for fast & friendly support. View our Terms of Service at: https://maplemedia.io/terms-of-service/ View our Privacy Policy at: https://maplemedia.io/privacy/

Classic minesweeper game, redefined by AI. Our minesweeper game is one of its own kind in the market. We leverage AI technology to achieve various improvement on top of the classic minesweeper game. - Exercise your brain and logic reasoning skills - 4 difficulty options - Fun experiences to unlock and daily rewards to claim. - Classic minesweeper redefined by AI * Redefined by AI * 1. Model the minesweeper game as a Constraint Satisfaction Problem (CSP) [a] 2. Precompute the heuristic score for each valid move using multiple state-of-the-art algorithms [b], to yield a stack rank of 10 (or less) best moves. 3. Based on the heuristic difference between the optimal move and the actual move by the user, we use our pre-defined probability (non-uniform distribution) to control the mine position (by ad-hoc switching cells on the board). 4. At the end of the game, further tune the mine-switching probability using win/lose ratio as an input [c], to ensure a custom tailored gaming experience that’s challenging but not frustrating. References: [a] https://inst.eecs.berkeley.edu/~cs188/fa18/assets/slides/lec4/FA18_cs188_lecture4_CSPs_6pp.pdf [b] https://dash.harvard.edu/bitstream/handle/1/14398552/BECERRA-SENIORTHESIS-2015.pdf?sequence=1 [c] https://en.wikipedia.org/wiki/Reinforcement_learning