Page 24 - Socially Intelligent Agents Creating Relationships with Computers and Robots

Creating Relationships with Computers and Robots 7

an Air Force combat task. Focusing on traits ‘anxiety’, ‘aggressiveness’, and
‘obsessiveness’, the prototype uses a knowledge-based approach to assess and
adapt to the pilot’s anxiety level by means of different task-specific compen-
satory strategies implemented in terms of specific GUI adaptations. One of the
focal goals of this research is to increase the realism of social intelligent agents
in situations where individual adaptation to the user is crucial, as in the critical
application reported here.
Chapter 7, by Sebastiano Pizzutilo, Berardina De Carolis, and Fiorella De
Rosis discusses how cooperative interface agents can be made more believable
when endowed with a model that combines the communication traits described
in the Five Factor Model of personality (e.g., ‘extroverted’ versus ‘introverted’)
with some cooperation attitudes. Cooperation attitudes refer in this case to the
level of help that the agent provides to the user (e.g., an overhelper agent, a
literal helper agent), and the level of delegation that the user adopts towards
the agent (e.g., a lazy user versus a ‘delegating-if-needed’ one). The agent
implements a knowledge-based approach to reason about and select the most
appropriate response in every context. The authors explain how cooperation
and communication personality traits are combined in an embodied animated
character (XDM-Agent) that helps users to handle electronic mail using Eu-
dora.
In chapter 8, Lola Cañamero reports the rationale underlying the construc-
tion of Feelix, a very simple expressive robot built from commercial LEGO
technology, and designed to investigate (facial) emotional expression for the
sole purpose of social interaction. Departing from realism, Cañamero’s ap-
proach advocates the use of a ‘minimal’ set of expressive features that allow
humans to recognize and analyze meaningful basic expressions. A clear causal
pattern of emotion elicitation—in this case based on physical contact—is also
necessary for humans to attribute intentionality to the robot and to make sense
of its displays. Based on results of recognition tests and interaction scenarios,
Cañamero then discusses different design choices and compares them with
some of the guidelines that inspired the design of other expressive robots, in
particular Kismet (cf. chapter 18). The chapter concludes by pointing out some
of the ‘lessons learned’ about emotion from such a simple robot.
Chapter 9, by Valery Petrushin, investigates how well people and computers
can recognize emotions in speech, and how to build an agent that recognizes
emotions in speech signal to solve practical, real-world problems. Motivated
by the goal of improving performance at telephone call centers, this research
addresses the problem of detecting emotional state in telephone calls with the
purpose of sorting voice mail messages or directing them to the appropriate
person in the call center. An initial research phase, reported here, investigated
which features of speech signal could be useful for emotion recognition, and
explored different machine learning algorithms to create reliable recognizers.