Rationalization: A neural machine translation approach to generating natural language explanations
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018•dl.acm.org
We introduce\em 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 …
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 …
We introduce \em 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.
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