either all fuse tokens are used up, resulting in a game loss, or all 5's have been played successfully, leading to a game win. Otherwise, play continues until the deck runs out, 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. Reception Hanabi received positive reviews. Board Game Quest awarded the game four and a half stars, praising its uniqueness, accessibility and engagement. Similarly, The Opinionated Gamers also praised the game's engagement and addictiveness. It won several awards, including the 2013 Spiel des Jahres winner and 2013 Fairplay À la carte Award winner. Hanabi also placed sixth place in the 2013 Deutscher Spiele Preis. 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. However, this gap has narrowed significantly as of 2020, with the Simplified Action Decoder achieving scores around 24. 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