Evaluating and Modelling Hanabi-Playing 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.
Keywords
Cite
@article{arxiv.1704.07069,
title = {Evaluating and Modelling Hanabi-Playing Agents},
author = {Joseph Walton-Rivers and Piers R. Williams and Richard Bartle and Diego Perez-Liebana and Simon M. Lucas},
journal= {arXiv preprint arXiv:1704.07069},
year = {2017}
}
Comments
Proceedings of the IEEE Conference on Evolutionary Computation (2017)