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Evaluating and modelling Hanabi-playing agents

Walton Rivers, J and Williams, PR and Bartle, R and Perez Liebana, D and Lucas, SM (2017) Evaluating and modelling Hanabi-playing agents. In: IEEE Congress on Evolutionary Computation (CEC), 2017, 2017-06-05 - 2017-06-08, San Sebastian, Spain.

1704.07069v1.pdf - Accepted Version

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Notes: Proceedings of the IEEE Conference on Evolutionary Computation (2017)
Uncontrolled Keywords: cs.AI
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 08 Sep 2017 13:00
Last Modified: 23 Sep 2022 18:50

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