Over the past couple of weeks, Matthew and I have been trying to narrow down our idea for capstone. We have settled on a "Modular" AI that can play First Person Shooters or other similar video games. However, we decided to put a slight twist on the idea of an AI playing games. Most of the AIs that are currently out have more information than they should possibly have at that time, like the location of players. We decided that our AI would only have information that would be accessible to a human player. We also noticed that many of the "PlayerAIs" out there are reactionary, not planning. While reacting is a key part to many of these games, so is strategy. We want to create an AI that thinks, at least a little bit, about that actions it is making or should make.
Since narrowing down our topic we have split off and started looking at different existing technologies and research that could help us understand and create this project. I decided to look at some computer vision tactics, mainly Scene Segmentation.
Scene Segmentation is a sort of advanced form of Object Recognition that tries to label objects but also understand their boundaries by looking at a particular scene(2D). The image to the right was taken from here. Which is an implantation of SegNet. While higher confidence versions of scene segmentation do exist, I currently find Segnet the most useful because the source code is on the internet and can be found on GitHub.
Other implementations of Scene segmentation are very impressive such as the video included show the power that this system could have. It is reasonable to say that we could use this in our final product.
I also started to look at the basics of audio triangulation and speech recognition. Since human players have access to this data, I thought it would be interesting to get the AI to behave based on sound. If the AI 'hears' another character it should react accordingly.