기술
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
더보기 ↓ 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
새로운 기능
- 버전: 9.2
- 업데이트:
- Some bug fixes.
가격
- 오늘: 무료
- 최소: 무료
- 최대: 무료
가격 추적
포인트
순위
찾을 수 없음 ☹️
목록
찾을 수 없음 ☹️
리뷰
첫 번째 리뷰를 남겨보세요!
추가 정보
- 카테고리:
iOS 게임
›퍼즐
- 운영체제:
iPad
,iPhone
,iOS 16.0 이상
- 크기:
45 Mb
- 지원되는 언어:
Arabic
,Catalan
,Chinese
,Croatian
,Czech
,Danish
,Dutch
,English
,Finnish
,French
,German
,Greek
,Hebrew
,Hindi
,Hungarian
,Indonesian
,Italian
,Japanese
,Korean
,Malay
,Norwegian
,Polish
,Portuguese
,Romanian
,Russian
,Slovak
,Spanish
,Swedish
,Thai
,Turkish
,Ukrainian
,Vietnamese
- 콘텐츠 등급:
4+
- App Store 개의 평가:
4.6
(6,316) - 업데이트:
- 출시일:
주소록
- 웹사이트: https://ggfen.com
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