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

@article{Cox2015HowTM,
  title={How to Make the Perfect Fireworks Display: Two Strategies for Hanabi},
  author={Christopher L. Cox and Jessica De Silva and Philip DeOrsey and Franklin Kenter and Troy Retter and Josh Tobin},
  journal={Mathematics Magazine},
  year={2015},
  volume={88},
  pages={323 - 336}
}
Summary The game of Hanabi is a multiplayer cooperative card game that has many similarities to a mathematical “hat guessing game.” In Hanabi, a player does not see the cards in her own hand and must rely on the actions of the other players to determine information about her cards. This article presents two strategies for Hanabi. These strategies use different encoding schemes, based on ideas from network coding, to efficiently relay information. The first strategy allows players to effectively… 
Hanabi is NP-hard, even for cheaters who look at their cards
Hanabi is NP-complete, Even for Cheaters who Look at Their Cards
TLDR
A simplified mathematical model of a single-player version of the cooperative card game Hanabi is introduced, and several complexity results are shown: the game is intractable in a general setting even if the authors forego with the hidden information aspect of the game.
Playing Hanabi Near-Optimally
TLDR
This study shows that the game of Hanabi, a multi-player cooperative card game in which a player sees the cards of the other players but not his own cards, can be played near-optimally by the computer.
Wait a second: playing Hanabi without giving hints
TLDR
This paper presents Hanabi agents that utilize timing as a covert channel so effectively that they can eschew the communicative actions provided by the game entirely and provides its context in the area of security, and an outlook on how it could be related to human behavior in future work.
Evolving Agents for the Hanabi 2018 CIG Competition
TLDR
A genetic algorithm is developed that builds rule-based agents by determining the best sequence of rules from a fixed rule set to use as strategy and achieves scores superior to previously published research for the mirror and mixed evaluation of agents.
Diverse Agents for Ad-Hoc Cooperation in Hanabi
TLDR
Quality Diversity algorithms are proposed as a promising class of algorithms to generate populations for this purpose and an initial implementation of an agent generator based on this idea is shown.
Aspects of the Cooperative Card Game Hanabi
TLDR
This work examines the cooperative card game Hanabi, shows some combinatorial properties, and develops AI (Artificial Intelligence) players that use rule-based and Monte Carlo methods.
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi
TLDR
Quality Diversity algorithms are proposed as a promising class of algorithms to generate diverse populations for this purpose, and a population of diverse Hanabi agents is generated using MAP-Elites.
Behavioral Evaluation of Hanabi Rainbow DQN Agents and Rule-Based Agents
TLDR
A key finding is that while most agents only learn to play well with partners seen during training, one particular agent leads the Rainbow algorithm towards a much more general policy.
Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners
TLDR
It is shown that agents trained through self-play using the popular RainbowDQN architecture fail to cooperate well with simple rule-based agents that were not seen during training and, conversely, if agents are trained to play with any individualRule-based agent, or even a mix of these agents, they fail to achieve good self- play scores.
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