Consider a situation where a robot has to move a teapot from kitchen to a room. It can do this task by performing some actions. The problem is that in practical situations we have huge number of actions to consider. The solution therefore is to select relevant actions from the action set. Like in our example, for transferring teapot from kitchen to a room, picking up microwave is less relevant as compared to picking up teapot. For more information, read this.
In this project we make an attempt to solve the situation described above by learning relevancy of actions. We use Naive Bayes Classifier to learn the relevancy of actions.