Abstract-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 hiddeninformation, 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. Hanabi is a co-operative, partially-observable [1] board game which in 2013 won the prestigious Spiel des Jahres award for best board game of the year. For the reasons outlined below, it has featured in a number of recent academic publications. This paper explores whether the use of agent modelling can lead to an improvement in strength for agents playing the game. Hanabi has a number of interesting features that make it a good choice for research in the field of agent modelling. Firstly, the domain is a co-operative one, in that the agents must work together to achieve a shared goal. This disfavours agents that behave greedily: for example, helping another player score a point is better than playing a risky card that might end the game. Secondly, its rules build in well-defined communication actions. These use a resource that regulates communication and must be managed by the agents. Finally, the game has hidden information, with no one player able to see the entire game state. This is a source of complexity for agents, because imperfect information needs to be reasoned about intelligently. Note that Hanabi has been proven to be NP-Complete even when players have perfect information [2] . A number of rule-based approaches for designing agents that can play Hanabi have been presented in the literature, however there have been few attempts to employ more general strategies. In this paper, we go some way towards redressing this imbalance. Furthermore, because the use of information about other players' strategies can help to inform human players in co-operative games, we also explore whether or not such information could help guide our general agents' decisions. Section I-A describes the rules of the game of Hanabi. Section II describes the agents that were implemented to play Hanabi under these rules.
Recently, Walton-Rivers et al. REF studied agent modeling in the card game Hanabi 8 and compared rule-based players with an ISMCTS player, which showed poor performance.
Evaluating and modelling Hanabi-playing agents
2017 IEEE Congress on Evolutionary Computation (CEC)
2017 IEEE Congress on Evolutionary Computation (CEC)
Computer Science