We present a new game, “MLA: Machine Learning Arena”, in which the player’s goal is to train a machine learning agent to win a boxing match. The game features multiple phases, fully animated characters for the player to control, and machine learning integration. We tackled several technical and design challenges, including: 1) Communicating machine learning progress through UI elements to the user, 2) Training an effective model for the game agent despite poor training examples from users, 3) Explaining key ideas about machine learning to players with no background in the field. We conduct user testing with 27 players to determine if they feel that the ML is learning from them. We found that 85 percent of players were able to distinguish between a random agent and the trained agent.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Ferguson, Grant
• Cattelona, Jordan Anthony
• Kreiselman, Justin R.

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-121219-103530

Advisor

• Whitehill, Jacob Richard
• Smith, Gillian Margaret

Year

• 2019

Date created

• 2019-12-12

Resource type

• Major Qualifying Project

Major

• Interdisciplinary

Rights statement

• In Copyright