In this experiment, a haptic glove with vibratory motors on the fingertips was tested against the standard HTC Vive controller to see if the additional vibrations provided by the glove increased immersion in common gaming scenarios where haptic feedback is provided. Specifically, two scenarios were developed: an explosion scene containing a small and large explosion and a box interaction scene that allowed the participants to touch the box virtually with their hand. At the start of this project, it was hypothesized that the haptic glove would have a significant positive impact in at least one of these scenarios. Nine participants took place in the study and immersion was measured through a post-experiment questionnaire. Statistical analysis on the results showed that the haptic glove did have a significant impact on immersion in the box interaction scene, but not in the explosion scene. In the end, I conclude that since this haptic glove does not significantly increase immersion across all scenarios when compared to the standard Vive controller, it should not be used at a replacement in its current state.
This project is a series of two YouTube videos that follow me learning new skills. The first is soldering, and the second is jumping a bicycle. The goal of this project is to use it to hone my cinematography skills and to inspire other beginners to try new things by highlighting my own trials and tribulations and being vulnerable.
Designing these agents to cover every case of human interaction is difficult, and usually
imperfect, as human players are capable of learning to overcome these agents in unintended
ways. Artificial intelligence is a growing field that seeks to solve problems by simulating
learning in specific environments. The aim of this paper is to explore the applications that the
self play learning branch of artificial intelligence may pose on game development in the future,
and to attempt to implement a working version of a self play agent learning to play a Pokemon
battle. Originally designed Pokemon battle behavior is often suboptimal, getting stuck making
ineffective or incorrect choices, so training a self play model to learn the strategy and structure of
Pokemon battles from a clean slate would result in an organic agent that would outperform the
original behavior of the computer controlled agents. Though unsuccessful in my implementation,
this paper serves as a record of the exploration of this field, and a log of what worked and what
did not, in order to benefit any future person interested in the same topics.
This is a difficult question for a variety of reasons. A major issue to consider is whether the students who play this game are actually learning the material, or simply improving at the game itself. If the game is not designed correctly, one could potentially learn to exploit game mechanics without applying knowledge of the material. While this person’s efficiency at completing the game quickly would suggest mastery of the topic, they may not actually be prepared to take a test on the subject. As such, it is important to thoroughly study the effectiveness of serious games before they are deployed to actual classrooms. This study will do just that with the game Vector Unknown, which was designed to help college students learn linear algebra.