  Value alignment is a property of an intelligent agent indicating that it can
only pursue goals and activities that are beneficial to humans. Traditional
approaches to value alignment use imitation learning or preference learning to
infer the values of humans by observing their behavior. We introduce a
complementary technique in which a value aligned prior is learned from
naturally occurring stories which encode societal norms. Training data is
sourced from the childrens educational comic strip, Goofus and Gallant. In this
work, we train multiple machine learning models to classify natural language
descriptions of situations found in the comic strip as normative or non
normative by identifying if they align with the main characters behavior. We
also report the models performance when transferring to two unrelated tasks
with little to no additional training on the new task.
