Towards Learning From Stories: An Approach to Interactive Machine Learning.
Brent E. Harrison, Mark Riedl · National Conference on Artificial Intelligence · 2016
In this work, we introduce a technique that uses stories to train virtual agents to exhibit believable behavior. This technique uses a compact representation of a story to define the space of acceptable behaviors and then uses this space to assign rewards to certain world states. We show the effectiveness of our technique with a case study in a modified gridworld environment called Pharmacy World. The results show that a reinforcement learning agent using Q-learning was able to learn a policy that results in believable behavior.