Minesweeper Classic Puzzles vs Minesweeper ~

Minesweeper is a single-player puzzle game. The objective of the game is to clear a rectangular board containing hidden "mines" or bombs without detonating any of them, with help from clues about the number of neighboring mines in each field. To win you need to determine the location of all mines. To achieve this goal, you have to click a square and if it is empty all adjacent empty squares will open up. If you ever click on a box containing a mine, it will explode and you'll lose. It is possible to plant a flag to mark the alleged location of a mine, using the button corresponding to the flag. If the clicked box is empty, all adjacent empty boxes will open recursively. To win, there must be no mines incorrectly marked with a flag. On the other hand, it is not necessary to mark all the mines to finish; you just need to determine all the empty boxes.

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