# Evaluating and Modelling Hanabi-Playing Agents

arXiv: https://arxiv.org/abs/1704.07069 · HTML: https://mlsanity.com/paper/1704.07069/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2017.

## Built on by (most cited first, 9 of 9)

- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](https://mlsanity.com/paper/2107.07630/) (2021)
- [Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination](https://mlsanity.com/paper/2201.12436/) (2022)
- [Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings](https://mlsanity.com/paper/2106.09086/) (2021)
- [Collaborative Agent Gameplay in the Pandemic Board Game](https://mlsanity.com/paper/2103.11388/) (2021)
- [Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operations](https://mlsanity.com/paper/2106.11345/) (2021)
- [Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic](https://mlsanity.com/paper/2103.15090/) (2021)
- [Behavioral Differences is the Key of Ad-hoc Team Cooperation in Multiplayer Games Hanabi](https://mlsanity.com/paper/2303.06775/) (2023)
- [JaxAHT: A JAX-Based Library for Ad Hoc Teamwork](https://mlsanity.com/paper/2609.13716/) (2026)
- [Measuring Successful Cooperation in Human-AI Teamwork: Development and Validation of the Perceived Cooperativity and Teaming Perception Scales](https://mlsanity.com/paper/2604.24461/) (2026)

## Abstract (arXiv)

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.