One day it can play Zork :)

Very nice paper from famous CSAIL laboratory (sorry for tautology) at MIT:

Narasimhan et al (2015) Language Understanding for Text-based Games Using Deep Reinforcement Learning

In two words: the paper describes a way to teach a machine to perceive game situation through a textual description and make correspondent actions. This is a tough task to do due to complexity of the natural language processing. The algorithm makes use of the Deep Q-Network by Mnih at al (2013 and 2015) and Long-Short Term Memory units. Interestingly an experiment for the transfer learning is also done, that uses networks parameters learned from settings A in the setting B, showing premise of the approach.