Hanabi is a cooperative card game with hidden information that has won important awards in the industry and received some recent academic attention. A two-track competition of agents for the game will take place in the 2018 CIG conference. In this paper, we develop a genetic algorithm that builds rule-based agents by determining the best sequence of rules from a fixed rule set to use as strategy. In three separate experiments, we remove human assumptions regarding the ordering of rules, add new, more expressive rules to the rule set and independently evolve agents specialized at specific game sizes. As result, we achieve scores superior to previously published research for the mirror and mixed evaluation of agents.
Optimal play of (generalized) Hanabi has been proven to be an NP-Complete problem by Baffier . @cite_15 , even if we remove all hidden information. Due to this complexity, most research in Hanabi-playing agents @cite_17 @cite_3 @cite_2 @cite_13 focuses on one of two methods (or a combination of both): Rule-based agents, which go through an ordered list of heuristics (such as play a card that is known to be playable, discard a random card, hint a playable card, etc.) and play the first one that is applicable. Search-based agents, which use a model of the other players' behavior to search for the action that leads to best results, such as Monte-Carlo Tree Search (MCTS) @cite_19 , or to search for previous states that are consistent with the hints received by another player.
@cite_3 @cite_19 @cite_2 @cite_15 @cite_13 @cite_17
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We examine the cooperative card game Hanabi. Players can only see the cards of the other players, but not their own. Using hints partial information can be revealed. We show some combinatorial properties, and develop AI (Artificial Intelligence) players that use rule-based and Monte Carlo methods. Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work. Cooperative games with partial observability are a challenging domain for AI research, especially when the AI should cooperate with a human player. In this paper we investigate one such game, the award-winning card game Hanabi, which has been studied by other researchers before. We present an agent designed to play better with a human cooperator than these previous results by basing it on communication theory and psychology research. To demonstrate that our agent performs better with a human cooperator we ran an experiment in which 224 participants played one or more games of Hanabi with different AIs, and will show that our AI scores higher than previously published work in such a setting. This paper studies a cooperative card game called Hanabi from an algorithmic combinatorial game theory viewpoint. The aim of the game is to play cards from @math to @math in increasing order (this has to be done independently in @math different colors). Cards are drawn from a deck one by one. Drawn cards are either immediately played, discarded or stored for future use (overall each player can store up to @math cards). We introduce a simplified mathematical model of a single-player version of the game, and show several complexity results: the game is intractable in a general setting, but becomes tractable (and even linear) in some interesting restricted cases (i.e., for small values of @math and @math ). Agent modelling involves considering how other agents will behave, in order to influence your own actions. In this paper, we explore the use of agent modelling in the hidden-information, collaborative card game Hanabi. We implement a number of rule-based agents, both from the literature and of our own devising, in addition to an Information Set Monte Carlo Tree Search (IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new, predictor version that uses a model of the agents with which it is paired. We observe a significant improvement in game-playing strength from this agent in comparison to IS-MCTS, resulting from its consideration of what the other agents in a game would do. In addition, we create a flawed rule-based agent to highlight the predictor's capabilities with such an agent.