Page 171 - Socially Intelligent Agents Creating Relationships with Computers and Robots

154 Socially Intelligent Agents

sion [12] or vocal expression [10, 5] to be very useful in mapping Kismet’s
emotive states to its face actuators and its articulatory-based speech synthe-
sizer. Results from various forced-choice and similarity studies suggest that
Kismet’s emotive facial expressions and vocal expressions are readable.
Furthermore, we have learned that artistic insights complement these sci-
entific findings in very important ways. A number of animation guidelines
and techniques have been developed for achieving life-like, believable, and
compelling animation [13, 11]. These rules of thumb are designed to create
behavior that is rich and interesting, yet easily understandable to the human
observer. For instance, animators take a lot of care in drawing the audience’s
attention to the right place at the right time. To enhance the readability and
understandability of Kismet’s behavior, Kismet’s expression and gaze precede
its behavioral response to make its behavior understandable and predictable to
the human who interacts with it. People naturally tend to look at what Kismet
is looking at. They observe the expression on its face to see how the robot will
respond towards it. If the robot has a frightened expression, the observer is
not surprised to witness a fleeing response soon afterwards. If they are behav-
ing towards the robot in a way that generates a negative expression, they soon
correct their behavior.
By incorporating these scientific and artistic insights, we found that people
intuitively and naturally use Kismet’s expressive feedback to tune their perfor-
mance in the exchange. We have learned that through a process of entraining
to the robot, both the human and robot benefit: the person enjoys the easy in-
teraction while the robot is able to perform effectively within its perceptual,
computational, and behavioral limits. Ultimately, these cues will allow hu-
mans to improve the quality of their instruction. For instance, human-robot
entrainment can be observed during turn-taking interactions. They start to use
shorter phrases, wait longer for the robot to respond, and more carefully watch
the robot’s turn-taking cues. The robot prompts the other for his/her turn by
craning its neck forward, raising its brows, and looking at the person’s face
when it’s ready for him/her to speak. It will hold this posture for a few seconds
until the person responds. Often, within a second of this display, the subject
does so. The robot then leans back to a neutral posture, assumes a neutral ex-
pression, and tends to shift its gaze away from the person. This cue indicates
that the robot is about to speak. The robot typically issues one utterance, but
it may issue several. Nonetheless, as the exchange proceeds, the subjects tend
to wait until prompted. This allows for longer runs of clean turns before an
interruption or delay occurs in the robot-human proto-dialogue.

Interpretation of Human’s Social Cues. During social exchanges, the
person sends social cues to Kismet to shape its behavior. Kismet must be
able to perceive and respond to these cues appropriately. By doing so, the