Date of Award

12-2020

Culminating Project Type

Starred Paper

Degree Name

Computer Science: M.S.

Department

Computer Science and Information Technology

College

School of Science and Engineering

First Advisor

Bryant Julstrom

Second Advisor

Donald Hamnes

Third Advisor

Jie Meichsner

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Keywords and Subject Headings

Artificial Intelligence, Evolutionary Algorithm, Gameplay, Machine Learning, Neural Networks, Q-Learning

Abstract

Hanabi is a cooperative card game with hidden information that requires cooperation and communication between the players. For a machine learning agent to be successful at the Hanabi, it will have to learn how to communicate and infer information from the communication of other players. To approach the problem of Hanabi the machine learning methods of Q-learning and Evolutionary algorithm are proposed as potential solutions. The agents that were created using the method are shown to not achieve human levels of communication.

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