Narrow Artificial Intelligence with Machine Learning for Real-Time
  Estimation of a Mobile Agents Location Using Hidden Markov Models
  We propose to use a supervised machine learning technique to track the
location of a mobile agent in real time. Hidden Markov Models are used to build
artificial intelligence that estimates the unknown position of a mobile target
moving in a defined environment. This narrow artificial intelligence performs
two distinct tasks. First, it provides real-time estimation of the mobile
agent's position using the forward algorithm. Second, it uses the Baum-Welch
algorithm as a statistical learning tool to gain knowledge of the mobile
target. Finally, an experimental environment is proposed, namely a video game
that we use to test our artificial intelligence. We present statistical and
graphical results to illustrate the efficiency of our method.
