Minesweeper ~ is made and published by Yi Zeng. AppBrain is an independent app data site and isn't affiliated with Yi Zeng. The app name, icon, screenshots and description belong to Yi Zeng and are shown as published on the App Store.
AppBrain's verdict
EstablishedLoved by users- Rating on App Store
- 4.64
- 8,353 ratings
- Last updated
- August 30, 2023
Users love it: 4.6 stars from 8,353 ratings.
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Releases
Updates seen on the App Store- Version
- 9.2
- Launched
- June 2020
No changelog data
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Technology
Found in the app by AppBrain- Price
- Free
- App size
- 44.6 MB
- Content rating
- Everyone
Technologies used by Minesweeper ~
About the app and developer
About Minesweeper ~
Minesweeper ~ is a games app developed by Yi Zeng.
User Ratings
Minesweeper ~ is rated 4.64 out of 5 stars. The rating is based on 8.4 thousand ratings.
App Information
Minesweeper ~ is freely available from the App Store. The app size is 44.61 MB. The latest version available is 9.2. The last update was on August 30, 2023.
Technical Requirements
The app has a content rating of Everyone. The app has been available on the App Store since June 2020.
Developer information for Yi Zeng
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Store listing
By Yi Zeng, from the App Store
Description by Yi Zeng Written by the developer, not by AppBrain. Click to read.
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