2 research outputs found
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations
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.Comment: 9 pages, 4 figures; added human evaluation section; added author;
changed author order-Upol Ehsan and Brent Harrison both contributed equally
to this wor
Increasing replayability with deliberative and reactive planning
Opponent behavior in today's computer games is often the result of a static set of Artificial Intelligence (AI) behaviors or a fixed AI script. While this ensures that the behavior is reasonably intelligent, it also results in very predictable behavior. This can have an impact on the replayability of entertainment-based games and the educational value of training-based games. This paper proposes a move away from static, scripted AI by using a combination of deliberative and reactive planning. The deliberative planning (or Strategic AI) system creates a novel strategy for the AI opponent before each gaming session. The reactive planning (or Tactical AI) system executes this strategy in real-time and adapts to the player and the environment. These two systems, in conjunction with a future automated director module, form the Adaptive Opponent Architecture. This paper describes the architecture and the details of the deliberative and reactive planning components