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https://github.com/mkhrapov/sansumoku
iOS implementation of an original game.
https://github.com/mkhrapov/sansumoku
game-development ios swift
Last synced: 2 months ago
JSON representation
iOS implementation of an original game.
- Host: GitHub
- URL: https://github.com/mkhrapov/sansumoku
- Owner: mkhrapov
- License: apache-2.0
- Archived: true
- Created: 2019-08-23T16:01:05.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-01-19T19:17:13.000Z (almost 5 years ago)
- Last Synced: 2024-08-03T17:17:17.275Z (5 months ago)
- Topics: game-development, ios, swift
- Language: Swift
- Homepage: https://www.sansumoku.com
- Size: 1.62 MB
- Stars: 12
- Watchers: 5
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- fucking-open-source-ios-apps - Sansumoku - player board game. A mash-up of Sudoku and Ultimate Tic-Tac-Toe (Game / Cryptocurrency)
README
# Sansumoku
An original board game for 2 players, a mash-up of Sudoku and Ultimate Tic-Tac-Toe. It is implemented as an iOS app in Swift and C/C++. For user facing features please see the [Sansumoku website](https://www.sansumoku.com/). Download [Sansumoku on the App Store](https://apps.apple.com/app/id1458250030).
## For developers
What would you find interesting in this repo as a developer?
- Custom board is implemented using drawing in Core Graphics
- A number of AI Engines implemented in C and C++, so you can learn how to integrate reasonably complex C and C++ code with Swift.
- [Monte Carlo Tree Search](https://en.wikipedia.org/wiki/Monte_Carlo_tree_search) algorithm. I gave a talk about Monte Carlo Tree
Search at the Austin Algorithms Meetup. [PDF Slides](extra/MonteCarloTreeSearch.pdf).
- AI Engine implemented using Apple's [GameplayKit](https://developer.apple.com/documentation/gameplaykit),
so you can learn how to use [GKMonteCarloStrategist](https://developer.apple.com/documentation/gameplaykit/gkmontecarlostrategist).![Sansumoku screenshot](img/sansumoku.png)