How to Make the Perfect Fireworks Display: Two Strategies for Hanabi

By C. Cox, J. De Silva, Philip DeOrsey, Franklin Kenter, Troy Retter, Josh Tobin. 2015

46 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

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In Computer Networks and Communications

Citations in the corpus

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning (2018, background): “Encoding schemes similar to the hat game essentially solves the 5-player case (Cox et al., 2015), but only achieve 17.8 points in the two-player setting (Bouzy, 2017).”
I See What You See: Integrating Eye Tracking into Hanabi Playing Agents (2018, background): “Cox et al. (2015) have devised a logical/mathematical protocol to convey a large amount of information using the limited communication Hanabi allows, scoring close to a perfect score in most games.”
Re-determinizing Information Set Monte Carlo Tree Search in Hanabi (2019, methodology): “2015 [22] have a very different approach that uses hat-guessing to pass on information to other players.”
Evolving Agents for the Hanabi 2018 CIG Competition (2018, unknown): “[13].”
Diverse Agents for Ad-Hoc Cooperation in Hanabi (2019, methodology): “On the other hand, a pool of very high-performing self-play agents with sophisticated conventions (such as the ones developed in [20], [21] or learned by Reinforcement Learning in [8], [18]) might resemble more a puzzle-solving challenge where the exact convention in use must be identiﬁed to…”
Hanabi is NP-complete, Even for Cheaters who Look at Their Cards (2016, background): “Most of the existing research [7, 21] proposes diﬀerent strategies so that players can share information and collectively play as many cards as possible.”
Evaluating and modelling Hanabi-playing agents (2017, methodology): “Cox [16] derives strategies for the game of Hanabi using the hat guessing game as inspiration.”
Implicit Communication of Actionable Information in Human-AI teams (2019, background): “For example, Bouzy [8] and Cox et al. [23] use ideas from hat guessing games to get near-optimal scores.”