  We introduce AI rationalization, an approach for generating explanations of
autonomous system behavior as if a human had performed the behavior. We
describe a rationalization technique that uses neural machine translation to
translate internal state-action representations of an autonomous agent into
natural language. We evaluate our technique in the Frogger game environment,
training an autonomous game playing agent to rationalize its action choices
using natural language. A natural language training corpus is collected from
human players thinking out loud as they play the game. We motivate the use of
rationalization as an approach to explanation generation and show the results
of two experiments evaluating the effectiveness of rationalization. Results of
these evaluations show that neural machine translation is able to accurately
generate rationalizations that describe agent behavior, and that
rationalizations are more satisfying to humans than other alternative methods
of explanation.
