Evaluating and Modelling Hanabi-Playing Agents
This work addresses agent modeling for collaborative games like Hanabi, but it is incremental as it builds on existing methods and focuses on a specific domain.
The paper tackled the problem of agent modeling in the hidden-information collaborative game Hanabi by developing a predictor version of an Information Set Monte Carlo Tree Search agent, which significantly improved game-playing strength compared to the original IS-MCTS agent.
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.