# Evaluating and Modelling Hanabi-Playing Agents

## TL;DR

This paper investigates agent modelling in Hanabi, demonstrating that a predictor-based IS-MCTS agent significantly outperforms standard IS-MCTS by considering other agents' behaviors, with implications for collaborative AI strategies.

## Contribution

Introduces a predictor-enhanced IS-MCTS agent for Hanabi, showing improved performance by modeling other agents' actions, and highlights the potential of agent modelling in collaborative games.

## Key findings

- Predictor-based IS-MCTS outperforms standard IS-MCTS.
- Agent modelling improves game-playing strength.
- The predictor effectively models flawed and rule-based agents.

## 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 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.