Hanabi is a cooperativecard game created by French game designer Antoine Bauza published in 2010 by Asmodée Éditions in which players, aware of other players' cards but not their own, attempt to play a series of cards in a specific order to set off a simulated fireworks show. Players are limited in the types of information they may give to other players, and in the total amount of information that can be given during the game. In 2013, Hanabi won the Spiel des Jahres, a prestigious industry award for best board game of the year.
Gameplay
The Hanabi deck contains cards in five suits : three 1's, two each of 2's, 3's, and 4's, and one 5. The game begins with 8 available information tokens and 3 fuse tokens. To start the game, players are dealt a hand containing five cards. As in Indian poker, players can see each other's cards but they cannot see their own. Play proceeds around the table; each turn, a player must take one of the following actions:
Give information: The player points out the cards of either a given number or a given suit in the hand of another player. The information given must be complete and correct. Giving information consumes one information token.
Discard a card: The player chooses a card from his hand and adds it to the discard pile, then draws a card to replace it. The discarded card is out of the game and can no longer be played. Discarding a card replenishes one information token.
Play a card: The player chooses a card from his hand and attempts to add it to the cards already played. This is successful if the card is a 1 in a suit that has not yet been played, or if it is the next number sequentially in a suit that has been played. Otherwise a fuse token is consumed and the misplayed card is discarded. Successfully playing a 5 of any suit replenishes one information token. Whether the play was successful or not, the player draws a replacement card.
Players lose immediately if all fuse tokens are gone, and win immediately if all 5's have been played successfully. Otherwise play continues until the deck becomes empty, and for one full round after that. At the end of the game, the values of the highest cards in each suit are summed, resulting in a total score out of a possible 25 points.
Variants
The game can be made easier by adding more information tokens, or more challenging by removing information or fuse tokens.
The deck comes with a 6th "rainbow" suit which can be added to the base game as either just an additional suit, or with the special rule that rainbow cards can not be pointed out as such, but instead must be treated as if they belonged to all other suits simultaneously.
The Royal Favor variant doesn't use scoring and players keep on playing even after the deck is gone, having potentially fewer cards in hands. Completing all fireworks till 5 is a win, anything else is a loss for all players. The game ends immediately when a player would start a turn with no cards in hand.
You can play Hanabi online at several different websites. The most popular are:
Computer Hanabi
Hanabi is a cooperative game of imperfect information. Computer programs which play Hanabi can either engage in "self-play" or "ad hoc team play". In self-play, multiple instances of the program play with each other on a team. They thus share a carefully honed strategy for communication and play, though of course they are not allowed to illegally share any information about each game with other instances of the program. In ad hoc team play, the program plays with other arbitrary programs or human players. A variety of computer programs have been developed by hand-coding rule-based strategies. The best programs, such as WTFWThat, achieved near-perfect results in self-play with five players, with an average score of 24.9 out of 25.
AI challenge
In 2019, DeepMind proposed Hanabi as an ideal game with which to establish a new benchmark for Artificial intelligence research in cooperative play. In self-play mode, the challenge is to develop a program which can learn from scratch to play well with other instances of itself. Such programs achieve only about 15 points per game as of 2019, far worse than hand-coded programs. Ad hoc team play is a far greater challenge for AI, because "Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground". Playing at human levels with ad hoc teams requires the algorithms to learn and develop communication conventions and strategies over time with other players via a theory of mind. Computer programs developed for self-play fail badly when playing on ad hoc teams, since they don't know how to learn to adapt to the way other players play. Deepmind released an open source code framework to facilitate research, called the Hanabi Learning Environment.
