The 2018 Hanabi competition

Walton-Rivers, Joseph; Williams, Piers R.; Bartle, Richard

doi:10.1109/cig.2019.8848008

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…Recently, there was a Hanabi competition that took place at the 2018 IEEE Computational Intelligence in Games Conference in Maastricht [52]. There were a total of five agents, three submissions and two samples .…”

Section: Prior Work On Hanabi Aimentioning

confidence: 99%

The Hanabi challenge: A new frontier for AI research

Bard¹,

Foerster

Chandar

et al. 2020

Artificial Intelligence

149

123

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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 with two to five players and imperfect information. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques for such theory of mind reasoning will not only be crucial for success in Hanabi, but also in broader collaborative efforts, 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. 6 One such equilibrium occurs when players do not intentionally communicate information to other players, and ignore what other players tell them (historically called a pooling equilibrium in pure signalling games [15], or a babbling equilibrium in later work using cheap talk [16]). In this case, there is no incentive for a player to start communicating because they will be ignored, and there is no incentive to pay attention to other players because they are not communicating.7 In pure signalling games where actions are purely communicative, policies are often referred to as communication protocols. Though Hanabi is not such a pure signalling game, when we want to emphasize the communication properties of an agent's policy we will still refer to its communication protocol. 8 We use the word convention to refer to the parts of a communication protocol or policy that interrelate. Technically, these can be thought of as constraints on the policy to enact the convention.

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Section: Prior Work On Hanabi Aimentioning

confidence: 99%

The Hanabi challenge: A new frontier for AI research

Bard¹,

Foerster

Chandar

et al. 2020

Artificial Intelligence

149

123

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“…CoG has many competitions. Those with recent papers at CoG include Hanabi Competition [15] which is based on the game Hanabi, a cooperative card game; Blood Bowl Competition [16] which is a board game competition for AI; VGC AI Competition [17] which is an AI competition based on Pok emon; and Carle's Game AI competition [18] which is a challenge in open-ended machine exploration and creativity. Now let us shift from competitions in CoG to sound design competitions outside of CoG.…”

Section: Competitions and Sound Design Competitionsmentioning

confidence: 99%

DareFightingICE Competition: A Fighting Game Sound Design and AI Competition

Khan¹,

Nguyen²,

Dai³

et al. 2022

Preprint

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This paper presents a new competition -at the 2022 IEEE Conference on Games (CoG) -called DareFightingICE Competition. The competition has two tracks: a sound design track and an AI track. The game platform for this competition is also called DareFightingICE, a fighting game platform. Dare-FightingICE is a sound-design-enhanced version of FightingICE, used earlier in a competition at CoG until 2021 to promote artificial intelligence (AI) research in fighting games. In the sound design track, participants compete for the best sound design, given the default sound design of DareFightingICE as a sample. Participants of the AI track are asked to develop their AI algorithm that controls a character given only sound as the input (blind AI) to fight against their opponent; a sample deep-learning blind AI will be provided by us. Our means to maximize the synergy between the two tracks are also described. This competition serves to come up with effective sound designs for visually impaired players, a group in the gaming community which has been mostly ignored. To the best of our knowledge, DareFightingICE Competition is the first of its kind within and outside of CoG.

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“…Research on AI agents playing Hanabi has been widely conducted in recent years (Osawa, 2015;Cox et al, 2015;Walton-Rivers et al, 2019;Sato and Osawa, 2019;Bard et al, 2020). Hanabi is a game where the results tend to differ depending on the combination of the teammate's strategy and your own.…”

Section: Background Of Hanabi Study: a Unique Testbed For Analyzing Human Cooperationmentioning

confidence: 99%

“…Hanabi is a game where the results tend to differ depending on the combination of the teammate's strategy and your own. From 2018, a competition was held as a part of the Computational Intelligence and Games (CIG), an international conference on computer games (Walton-Rivers et al, 2019). There are two types of competitions: one in which the same agents collaborate with each other, and the other in which different agents collaborate with each other.…”

Section: Background Of Hanabi Study: a Unique Testbed For Analyzing Human Cooperationmentioning

confidence: 99%

Emergence of Cooperative Impression With Self-Estimation, Thinking Time, and Concordance of Risk Sensitivity in Playing Hanabi

et al. 2021

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The authors evaluate the extent to which a user’s impression of an AI agent can be improved by giving the agent the ability of self-estimation, thinking time, and coordination of risk tendency. The authors modified the algorithm of an AI agent in the cooperative game Hanabi to have all of these traits, and investigated the change in the user’s impression by playing with the user. The authors used a self-estimation task to evaluate the effect that the ability to read the intention of a user had on an impression. The authors also show thinking time of an agent influences impression for an agent. The authors also investigated the relationship between the concordance of the risk-taking tendencies of players and agents, the player’s impression of agents, and the game experience. The results of the self-estimation task experiment showed that the more accurate the estimation of the agent’s self, the more likely it is that the partner will perceive humanity, affinity, intelligence, and communication skills in the agent. The authors also found that an agent that changes the length of thinking time according to the priority of action gives the impression that it is smarter than an agent with a normal thinking time when the player notices the difference in thinking time or an agent that randomly changes the thinking time. The result of the experiment regarding concordance of the risk-taking tendency shows that influence player’s impression toward agents. These results suggest that game agent designers can improve the player’s disposition toward an agent and the game experience by adjusting the agent’s self-estimation level, thinking time, and risk-taking tendency according to the player’s personality and inner state during the game.

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The 2018 Hanabi competition

Cited by 12 publications

References 13 publications

The Hanabi challenge: A new frontier for AI research

The Hanabi challenge: A new frontier for AI research

DareFightingICE Competition: A Fighting Game Sound Design and AI Competition

Emergence of Cooperative Impression With Self-Estimation, Thinking Time, and Concordance of Risk Sensitivity in Playing Hanabi

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