Watching my grandma play mahjong online and I was curious how the NPCs made their decisions.
- 3 or 4 player draw-and-discard game with 144 tiles based on Chinese characters and symbols.
- Match open pairs of identical tiles, remove from board, exposing the tiles under them for play.
- Game ends when all pairs of tiles have been removed from the board or no more exposed pairs remaining.
- Players get more realistic experiences and playable content in a game that involves skill, strategy, calculation, and chance.
- Old Hong Kong / Cantonese Mahjong [DEFAULT MODE]
- Competitive Mahjong International Standard
- Three-Player Mahjong [3-ka]
- Battle Mahjong [Player vs Cartoon NPC]
- Simples [108]
- Dots 36
- Bamboo 36
- Characters 36
- Honors [28]
- Winds - [North, West, South, East] 16
- Dragons - [Red, Green, White] 12
- Bonus [8]
- Flower - [Plum Blossom, Orchid, Chrysanthemum, Bamboo] 4
- Seasons - [Spring, Summer, Autumn, Winter] 4
- Mahjong Combos
- Heavenly Hand [天糊]
- Great Winds [大四喜]
- Great Dragons [大三元]
- All Kongs [十八羅漢]
- All Honor Tiles [字一色]
- Thirteen Orphans [十三幺]
- Nine Gates Hand [九蓮宝燈]
- Self Triplets [四暗刻]
- All in Triplets [對對糊]
- Mixed One Suit [混一色]
- All One Suit [清一色]
- Common Hand [平糊]
- Small Dragons [小三元]
- Small Winds [小四喜]
- ML Algorithms allows game to react and respond more dynamically and in more imaginative ways.
- Deep Neural Network with reinforcement learning implemented.
- Learn from its own game and top human players (via Classic Supervised Learning) where computations are made for every move or position.
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Non-Player Characters (NPCs)
- Algorithms playing as NPCs (with adjustable difficulties) respond to player’s actions in unique, unexpected ways.
- NPCs are non hard-coded.
- Train NPCs by imitating Top Mahjong Players to learn dynamic movements and actions.
- Natural Language Processing [NLP] to build realistic interactions in conversations. Key for Battle Mahjong [Player vs Cartoon NPC] style.
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Computational Modelling
- Complex game states modelled such that game can predict and alter downstream effects:
- Ex1: Team chemistry score calculated based on personalities of each gamer.
- Ex2: Morale of each player’s abilities as game is played in real-time.
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Game Aesthetics
- Ex: Computer Vision Algorithms used for mahjong textures and objects to render dynamically as player moves tiles on the board.
- DL Game Play
- AI will win through intelligence rather than faster mechanicals speed.
- Computers can programatically issue commands instantly whereas humans must physically move a mouse or hit the keyboard.
- Knowledge based hierarchy foundation with Goals, Strategies, Tactics, and Chains.
- Each objective inspects current game state and decides which lower level objective will be best to achieve it.
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Reinforcement Learning
- Markov Decision process to make decisions involving chain of if-then statements.
- Positive or Negative Reward.
- Algorithm will learn what actions will maximize the reward and which to be avoided.
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Deep Neural Network
- 3 Hidden layers of 120 neutrons.
- 3 Dropout layers to optimize generalization and reduce over-fitting.
- Input - State
- Output - Values related to Mahjong Actions
- Last layer uses Softmax Function to return probabilities.
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Deep Q-Learning
- Alpha-Beta Prunning
- AI weeds out bad moves.
- “Lookahead” Search Algorithms
- Open World Games
- Typically require thousands of hours of developer and artist time to render.
- Become more efficient using ML Path-Finding Algorithms.
- Have the potential to be unlimited in size
- Optimize game data with databases.
- Pre-Computed Moves for the beginning/end phrases of the game.
- Two Databases
- Opening DB
- Endgame DB
- Shiqi Gao et al. Building a Computer Mahjong Player via Deep Convolutional Neural Networks. https://arxiv.org/abs/1906.02146
- Open Spiel. A Framework For Reinforcement Learning in Games. https://deepmind.com/research/open-source/openspiel
- Pau Ramon Revilla. Open source multiplayer mahjong https://github.com/masylum/whatajong
- Rules of Mah-Jongg - Joseph Park Babcock
- Maajh: The American Version of the Ancient Chinese Game - Viola L. Cecil
- The Complete Book of Mah-jongg - Alan D. Millington