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Designing Sociable Robots 43

they have shaped the manner in which behaviors are organized, expressed, and arbitrated
among. Ethology also provides important insights as to how other systems influence be-
havior (i.e., motivation, perception, attention, and motor expression).
These ethology-based models of behavior are supplemented with models, theories, and
behavioral observations from developmental psychology and evolutionary perspectives. In
particular, these ideas have had a strong influence in the specification of the “innate endow-
ments” of the SNS, such as early perceptual skills (visual and auditory) and proto-social
responses. The field has also provided many insights into the nature of social interaction
and learning with a caregiver, and the importance of motivations and emotional responses
for this process.
Finally, models from psychology have influenced the design details of several systems.
In particular, psychological models of the attention system, facial expressions, the emotion
system, and various perceptual abilities have been adapted for Kismet’s SNS.

4.3 A Framework for the Synthetic Nervous System

The design details of each system and how they have incorporated concepts from these
scientific perspectives are presented in depth in later chapters. Here, I simply present a
bird’s eye view of the overall synthetic nervous system to give the reader a sense of how
the global system fits together. The overall architecture is shown in figure 4.1.
The system architecture consists of six subsystems. The low-level feature extraction sys-
tem extracts sensor-based features from the world, and the high-level perceptual system
encapsulates these features into percepts that can influence behavior, motivation, and motor
processes. The attention system determines what the most salient stimulus of the environ-
ment is at any time so that the robot can organize its behavior around it. The motivation
system regulates and maintains the robot’s state of “well-being” in the form of homeostatic
regulation processes and emotive responses. The behavior system implements and arbitrates
between competing behaviors. The winning behavior defines the current task (i.e., the goal)
of the robot. The robot has many behaviors in its repertoire, and several motivations to sa-
tiate, so its goals vary over time. The motor system carries out these goals by orchestrating
the output modalities (actuator or vocal) to achieve them. For Kismet, these actions are
realized as motor skills that accomplish the task physically, or as expressive motor acts that
accomplish the task via social signals.
Learning mechanisms will eventually be incorporated into this framework. Most likely,
they will be distributed through out the SNS to foster change within various subsystems as
well as between them. It is known that natural systems possess many different kinds of inter-
acting learning mechanisms (Gallistel, 1990). Such will be the case with the SNS concerning