First off, I don't really know much about machine learning.
In a virtual world, such as a video game like minecraft or an application like Google Street view, a user can navigate the world using the keyboard and mouse.
I'm looking to predict what interaction the user will perform. For example, the user might hold down
w for some period of time (to move forward) and then press and hold
a for a period of time to move left. Alternatively, the user may hold
w and use the mouse to turn and look up/down.
I've looked into N-Grams, and I can predict interaction to a certain degree, but I am not considering how long a user is not performing an action. I think performing
no action is an important indicator for correct interaction prediction as "no action" could itself be classified as an interaction. The duration of an interaction should also be important, but again, my not sure how to consider that in a model. My Ngram model simply considers the past 3 or 5 inputs.
I'd like to develop something which is very fast to predict interaction (this is key) and learns rapidly from mistakes. Ideally, it would be "Online" in that it constantly "Learns".
I'd be interested in hearing which models you think would be well suited to my task (are ngrams appropriate?), any examples which closely match my task and I can therefore adopt to suit my needs, any datasets containing user interaction and what I should look at to predict the mouse interaction.