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The Vision of Sociable Robots 11

interact with these digital pets within their virtual world via keyboard, mouse, buttons, etc.
Although still quite limited, the behavior and expression of these digital pets is produced by
a combination of pre-animated segments and internal states that determine which of these
segments should be displayed. Generally speaking, the observed behavior is familiar and
appealing to people if an intuitive relationship is maintained for how these states change
with time, how the human can influence them, and how they are subsequently expressed
through animation. If done well, people find these artifacts to be interesting and engaging
and tend to form simple relationships with them.

Socially Situated Learning
For a robot, many social pressures demand that it continuously learn about itself, those
it interacts with, and its environment. For instance, new experiences would continually
shape the robot’s personal history and influence its relationship with others. New skills
and competencies could be acquired from others, either humans or other agents (robotic
or otherwise). Hence, as with humans, robots must also be able to learn throughout their
lifetime. Much of the inspiration behind Kismet’s design comes from the socially situated
learning and social development of human infants.
Many different learning strategies are observed in other social species, such as learning
by imitation, goal emulation, mimicry, or observational conditioning (Galef, 1988). Some
of these forms of social learning have been explored in robotic and software agents. For
instance, learning by imitation or mimicry is a popular strategy being explored in humanoid
robotics to transfer new skills to a robot through human demonstration (Schaal, 1997) or to
acquire a simple proto-language (Billard & Dautenhahn, 2000). Others have explored social-
learning scenarios where a robot learns about its environment by following around another
robot (the model) that is already familiar with the environment. Billard and Dautenhahn
(1998) show how robots can be used in this scenario to acquire a proto-language to describe
significant terrain features.
In a more human-style manner, a robot could learn through tutelage from a human instruc-
tor. In general, it would be advantageous for a robot to learn from people in a manner that is
natural for people to instruct. People use many different social cues and skills to help others
learn. Ideally, a robot could leverage these same cues to foster its learning. In the next chap-
ter, I explore in depth the question of learning from people as applied to humanoid robots.

1.4 Book Overview

This section offers a road map to the rest of the book, wherein I present the inspiration, the
design issues, the framework, and the implementation of Kismet. In keeping with the infant-
caregiver metaphor, Kismet’s interaction with humans is dynamic, physical, expressive, and