Creating a non-person character (NPC) to play a game is becoming increasingly important. NPCs can be used in quality assurance to test a game before sending the game for certification. Being able to test a game in a way that mimics a human player would allow the test to be more accurate and would help […]

The goal of the project is to evaluate several clustering algorithms on players’ styles data in the context of Video Lottery Terminals (VLTs). The previous work has shown that by segmenting anonymous player data by sessions, and then clustering the sessions using the simple k-means algorithm, we can get a descriptive statistic on player styles, […]

The goal of this project is to train a machine learning model that can identify player’s personae using VLT (Video Lottery Terminal) data within a transactiontime limit. The personae are results of the previews MITACS project. Using unsupervised learning each playing session was associated with a playstyle. Identifying the playstyle as soon as possible is […]