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 [Tian Hu ]
  • Great Winds [Da Si Xi ]
  • Great Dragons [Da San Yuan ]
  • All Kongs [Shi Ba Luo Han ]
  • All Honor Tiles [Zi Yi Se ]
  • Thirteen Orphans [Shi San Yao ]
  • Nine Gates Hand [Jiu Lian Bao Deng ]
  • Self Triplets [Si An Ke ]
  • All in Triplets [Dui Dui Hu ]
  • Mixed One Suit [Hun Yi Se ]
  • All One Suit [Qing Yi Se ]
  • Common Hand [Ping Hu ]
  • Small Dragons [Xiao San Yuan ]
  • Small Winds [Xiao Si Xi ]

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Deep Q-Learning

  • Q-table matrix that updates Q-table based on the Prediction of Future Mahjong States.

  • Q-values updated according to the Bellman Equation.

• 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