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The Hanabi Challenge: A New Frontier for AI Research

02/01/2019
by   Nolan Bard, et al.
Google
University of Oxford
Carnegie Mellon University
54

From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay and imperfect information in a two to five player setting. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques capable of imbuing artificial agents with such theory of mind will not only be crucial for their success in Hanabi, but also in broader collaborative efforts, and especially those with human partners. To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.

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Code Repositories

hanabi.rs

State of the art Hanabi bots + simulation framework in rust


view repo

hanabi

Hanabi deck, game engine and AI.


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IN104_Francois_lfrancois144_Gorzkowski_BernardGorz

None


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Christine_Quach_Nina_Gregorio_Hanabi

hanabi project


view repo