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144 Socially Intelligent Agents

Figure 17.1. Carmen in Gina’s office.

Gina and Carmen interact through spoken dialog. In order to add realism
and maximize the expressive effect of this dialog, recorded dialog of voice ac-
tors is used instead of speech synthesis. A significant amount of variability
in the generated dialog is supported by breaking the recordings into meaning-
ful individual phrases and fragments. Additionally variability is achieved by
recording multiple variations of the dialog (in content and emotional expres-
sion). The agents compose their dialog on the fly. The dialog is also annotated
with its meaning, intent and emotional content. The agents use the annotations
to understand each other, to decide what to say, and more generally to inter-
act. The agents experience the annotations in order, so their internal state and
appearance can be in flux over the dialog segment.

3. Agent Architecture

The agent architecture is depicted in Figure 17.2. There are modules for
problem solving, dialog, emotional appraisal and physical focus. The problem
solving module is the agent’s cognitive layer, specifically its goals, planning
and deliberative reaction to world events. The dialog module models how to
use dialog to achieve goals. Emotional appraisal is how the agent emotionally
evaluates events (e.g., the dialog annotations). Finally, physical focus manages
the agent’s nonverbal behavior.
There are several novel pathways in the model worth noting. The agent’s
own acts feed back as input. Thus it is possible for the agent to say some-
thing and then emotionally and cognitively react to the fact that it has said it.
Emotional appraisal impacts problem solving, dialog and behavior. Finally,
there are multiple inputs to physical focus, from emotional appraisal, dialog
and problem solving, all competing for the agent’s physical resources (arms,
legs, mouth, head, etc.). For instance, the dialog module derives dialog that