SOCIALLY INTELLIGENT AGENTS Creating Relationships with Computers and Robots ppt

SOCIALLY INTELLIGENT AGENTS
Creating Relationships with
Computers and Robots
MULTIAGENT SYSTEMS,
ARTIFICIAL SOCIETIES,
AND SIMULATED ORGANIZATIONS
International Book Series
Series Editor: Gerhard Weiss
Technische Universität München
Editorial Board:
Kathleen M. Carley, Carnegie Mellon University, PA, USA
Yves Demazeau, CNRS Laboratoire LEIBNIZ, France
Ed Durfee, University of Michigan, USA
Les Gasser, University of Illinois at Urbana-Champaign, IL, USA
Nigel Gilbert, University of Surrey, United Kingdom
Michael Huhns, University of South Carolina, SC, USA
Nick Jennings, University of Southampton, UK
Victor Lesser, University of Massachusetts, MA, USA
Katia Sycara, Carnegie Mellon University, PA, USA
Gerhard Weiss, Technische Universität München, Germany (Series Editor)
Michael Wooldridge, University of Liverpool, United Kingdom
Books in the Series:
CONFLICTING AGENTS: Conflict Management in Multi-Agent
Systems, edited by Catherine Tessier, Laurent Chaudron and Heinz-Jürgen
Müller, ISBN: 0-7923-7210-7
SOCIAL ORDER IN MULTIAGENT SYSTEMS, edited by
Rosaria Conte and Chrysanthos Dellarocas, ISBN: 0-7923-7450-9
CONCEPTUAL MODELLING OF MULTI-AGENT
SYSTEMS: The CoMoMAS Engineering Environment, by Norbert
Glaser, ISBN: 1-4020-7061-6

SOCIALLY INTELLIGENT AGENTS
Creating Relationships with
Computers and Robots
Edited by
Kerstin Dautenhahn
University of Hertfordshire
Alan H. Bond
California Institute of Technology
Lola Cañamero
University of Hertfordshire
Bruce Edmonds
Manchester Metropolitan University
KLUWER ACADEMIC PUBLISHERS
Boston / Dordrecht / London
Print ISBN: 1-4020-7057-8
©2002 Kluwer Academic Publishers
New York, Boston, Dordrecht, London, Moscow
Print version ©2002 Kluwer Academic Publishers
Boston
All rights reserved
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mechanical, recording, or otherwise, without written consent from the Publisher.
Created in the United States of America
Visit Kluwer Online at:
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eBook ISBN: 0-306-47373-9
Contents
Contributing Authors ix
1
Socially Intelligent Agents: Creating Relationships with Computers and

Robots
1
Kerstin Dautenhahn, Alan Bond, Lola Cañamero and Bruce Edmonds
2
Understanding Social Intelligence
21
Per Persson, Jarmo Laaksolahti and Peter Lönnqvist
3
Modeling Social Relationship: An Agent Architecture for
Voluntary Mutual Control
29
Alan H. Bond
4
Developing Agents Who can Relate to Us: Putting Agents in Our Loop
via Situated Self-Creation
37
Bruce Edmonds
5
Party Hosts and Tour Guides: Using Nonverbal Social Cues in the Design
of Interface Agents to Support Human-Human Social Interaction
45
Katherine Isbister
6
Increasing SIA Architecture Realism by Modeling and Adapting to Af-
fect and Personality
53
Eva Hudlicka
7
Cooperative Interface Agents
61

Sebastiano Pizzutilo, Berardina De Carolis and Fiorella de Rosis
8
Playing the Emotion Game with Feelix: What Can a LEGO Robot Tell
Us about Emotion?
69
Lola Cañamero
vi Socially Intelligent Agents
9
Creating Emotion Recognition Agents for Speech Signal
77
Valery A. Petrushin
10
Social Intelligence for Computers: Making Artificial Entities Creative in
their Interactions
85
Juliette Rouchier
11
EgoChat Agent: A Talking Virtualized Agent that Supports Community
Knowledge Creation
93
Hidekazu Kubota and Toyoaki Nishida
12
Electric Elves: Adjustable Autonomy in Real-World Multi-Agent Environments
101
David V. Pynadath and Milind Tambe
13
Building Empirically Plausible Multi-Agent Systems: A Case Study of
Innovation Diffusion
109
Edmund Chattoe

14
Robotic Playmates: Analysing Interactive Competencies of
Children with Autism Playing with a Mobile Robot
117
Kerstin Dautenhahn, Iain Werry, John Rae, Paul Dickerson, Penny Stribling,
Bernard Ogden
15
Mobile Robotic Toys and Autism: Observations of Interaction
125
François Michaud and Catherine Théberge-Turmel
16
Affective Social Quest: Emotion Recognition Therapy for Autistic Children
133
Katharine Blocher and Rosalind W. Picard
17
Pedagogical Soap: Socially Intelligent Agents for Interactive Drama
141
Stacy C. Marsella
18
Designing Sociable Machines: Lessons Learned
149
Cynthia Breazeal
19
Infanoid: A Babybot that Explores the Social Environment
157
Hideki Kozima
20
Play, Dreams and Imitation in Robota
165
Aude Billard

Contents vii
21
Experiences with Sparky, a Social Robot
173
Mark Scheeff, John Pinto, Kris Rahardja, Scott Snibbe and Robert Tow
22
Socially Situated Planning
181
Jonathan Gratch
23
Designing for Interaction: Creating and Evaluating an Empathic Ambi-
ence in Computer Integrated Learning Environments
189
Bridget Cooper and Paul Brna
24
Me, My Character and the Others
197
Isabel Machado and Ana Paiva
25
From Pets to Storyrooms: Constructive Storytelling Systems
Designed with Children, for Children
205
Jaime Montemayor, Allison Druin, and James Hendler
26
Socially Intelligent Agents in Educational Games
213
Cristina Conati and Maria Klawe
27
Towards Integrating Plot and Character for Interactive Drama
221

Michael Mateas and Andrew Stern
28
The Cooperative Contract in Interactive Entertainment
229
R. Michael Young
29
Perceptions of Self in Art and Intelligent Agents
235
Nell Tenhaaf
30
Multi-Agent Contract Negotiation: Knowledge and Computation Complexities
243
Peyman Faratin
31
Challenges for Agent-Based Social Simulation of Multilateral
Negotiation
251
Scott Moss
32
Enabling Open Agent Institutions
259
Juan A. Rodríguez-Aguilar and Carles Sierra
viii Socially Intelligent Agents
33
Embodied Conversational Agents in E-Commerce Applications 267
Helen McBreen
Index 275
Contributing Authors
Aude Billard
Computer Science Department, University of Southern California, HNB, 3641

Wyatt Way, Los Angeles 90089, USA.
Katharine Blocher
Formerly of Massachusetts Institute of Technology, Media Laboratory, 4615
Huron Ave., San Diego, CA 92117, USA.
Alan H. Bond
California Institute of Technology, Department of Computer Science, Mailstop
256-80, Pasadena, CA 91125, USA.
Cynthia Breazeal
The Media Laboratory, Massachusetts Institute of Technology, 77 Massachusetts
Ave., NE18-5FL, Cambridge, MA 02139-4307, USA.

Paul Brna
Computer Based Learning Unit, University of Leeds, Leeds LS2 9JT, United
Kingdom.
Lola Cañamero
Adaptive Systems Research Group, Department of Computer Science, Univer-
sity of Hertfordshire, College Lane, Hatfield, Herts AL10 9AB, United King-
dom.
x Socially Intelligent Agents
Edmund Chattoe
University of Oxford, Department of Sociology, Littlegate House, St Ebbes,
Oxford, OX1 1PT, United Kingdom.

Cristina Conati
Department of Computer Science, University of British Columbia, 2366 Main
Mall, Vancouver, B.C. Canada V6T 1Z4.
Bridget Cooper
Computer Based Learning Unit, University of Leeds, Leeds LS2 9JT, United
Kingdom.
Kerstin Dautenhahn

Adaptive Systems Research Group, Department of Computer Science, Univer-
sity of Hertfordshire, College Lane, Hatfield, Herts AL10 9AB, United King-
dom.
Berardina Nadja De Carolis
Intelligent Interfaces, Department of Informatics, University of Bari, Via Orabo-
na 4, 70126 Bari, Italy.
Fiorella de Rosis
Intelligent Interfaces, Department of Informatics, University of Bari, Via Orabo-
na 4, 70126 Bari, Italy.
Paul Dickerson
University of Surrey Roehampton, School of Psychology and Counselling,
Whitelands College, West Hill, London, SW15 3SN, United Kingdom.

Allison Druin
Institute for Advanced Computer Studies, University of Maryland, College
Park, MD 742, USA.
Bruce Edmonds
Centre for Policy Modelling, Manchester Metropolitan University, Aytoun Build-
ing, Aytoun Street, Manchester, M1 3GH, United Kingdom.

Contributing Authors xi
Peyman Faratin
Center for Coordination Science, MIT Sloan School of Management, NE20-
336, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.

Jonathan Gratch
USC Institute for Creative Technologies, 13274 Fiji Way, Suite 600, Marina
del Rey, CA 90292, USA.
James A. Hendler
Institute for Advanced Computer Studies, University of Maryland, College

Park, MD 20742, USA.
Eva Hudlicka
Psychometrix Associates, Inc., 1805 Azalea Drive, Blacksburg, VA 24060,
USA.
Katherine Isbister
Finali Corporation, 3001 19th Street, 2nd floor, San Francisco, CA 94110,
USA.
Maria Klawe
Department of Computer Science, University of British Columbia, 2366 Main
Mall, Vancouver, B.C. Canada V6T 1Z4.
Hideki Kozima
Social Interaction Group, Keihanna Human Info-Communication Research Cen-
ter, Communications Research Laboratory, 2-2-2, Hikaridai, Seika-cho, Soraku-
gun, Kyoto 619-0289, Japan.
Hidekazu Kubota
Faculty of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo
113-8656, Japan.
Jarmo Laaksolahti
Swedish Institute of Computer Science (SICS), Box 1263,
SE-164 29 Kista, Sweden.
xii Socially Intelligent Agents
Peter Lönnqvist
Department of Computer and Systems Sciences, Stockholm University and
Royal Institute of Technology, Stockholm, Sweden.
Isabel Machado
Instituto de Engenharia de Sistemas e Computadores (INESC), Rua Alves
Redol 9, 1100 Lisboa, Portugal.
Stacy Marsella
USC Information Sciences Institute, 4676 Admiralty Way, Suite 1001, Marina
del Rey, CA 90292, USA.

Michael Mateas
Computer Science Department, Carnegie-Mellon University, 5000 Forbes Av-
enue, Pittsburgh, PA 15213, USA.
Helen McBreen
Centre for Communication Interface Research, Department of Electronics and
Electrical Engineering, University of Edinburgh, 80 South Bridge, EH1 1HN,
United Kingdom.
François Michaud
Department of Electrical Engineering and Computer Engineering, Université
de Sherbrooke, 2500 boul. Université, Sherbrooke (Québec) J1K 2R1, Canada.

Jaime Montemayor
Institute for Advanced Computer Studies, University of Maryland, College
Park, MD 20742, USA.
Scott Moss
Centre for Policy Modelling, Manchester Metropolitan University, Aytoun Build-
ing, Aytoun Street, Manchester, M1 3GH, United Kingdom.
Toyoaki Nishida
University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

Contributing Authors xiii
Bernard Ogden
Adaptive Systems Research Group, Department of Computer Science, Univer-
sity of Hertfordshire, College Lane, Hatfield, Herts AL10 9AB, United King-
dom.
Ana Paiva
Instituto de Engenharia de Sistemas e Computadores (INESC), Rua Alves
Redol 9, 1100 Lisboa, Portugal.
Valery A. Petrushin
Center for Strategic Technology Reasearch, Accenture, 3773 Willow Road,

Northbrook, IL 60062, USA.
Per Persson
Swedish Institute of Computer Science (SICS), Box 1263,
SE-164 29 Kista, Sweden.
Rosalind W. Picard
Massachusetts Institute of Technology, Media Laboratory, 20 Ames Street,
Cambridge, MA 02139, USA.
John P. Pinto
Formerly of Interval Research Corporation.
Sebastiano Pizzutilo
Intelligent Interfaces, Department of Informatics, University of Bari, Via Orabo-
na 4, 70126 Bari, Italy.
David V. Pynadath
Information Sciences Institute, University of Southern California, 4676 Admi-
ralty Way, Marina del Rey, CA 90292, USA.
John Rae
University of Surrey Roehampton, School of Psychology and Counselling,
Whitelands College, West Hill, London, SW15 3SN, United Kingdom.

xiv Socially Intelligent Agents
Krisnawan Rahardja
Formerly of Interval Research Corporation.
Juan A. Rodríguez-Aguilar
iSOCO Barcelona, Alcalde Barnils, 64-68 Edificio Testa - bl. A, 08190 Sant
Cugat Del Valles, Spain. Formerly of IIIA, Spanish Scientific
Research Council (CSIC), Spain.
Juliette Rouchier
GREQAM (CNRS), 2 Rue de la Charite, 13002 Marseille, France.

Mark Scheeff

Formerly of Interval Research Corporation.
Scott Sona Snibbe
Formerly of Interval Research Corporation.
Carles Sierra
Institut d’Investigació en Intel.ligència Artificial (IIIA), Spanish Scientific
Research Council (CSIC), Campus de la UAB, 08193 Bellaterra, Spain.

Andrew Stern
www.interactivestory.net,
Penny Stribling
University of Surrey Roehampton, School of Psychology and Counselling,
Whitelands College, West Hill, London, SW15 3SN, United Kingdom.

Milind Tambe
Information Sciences Institute, University of Southern California,
4676 Admiralty Way, Marina del Rey, CA 90292, USA.
Contributing Authors xv
Nell Tenhaaf
Department of Visual Arts, 232 Centre for Fine Arts, York University, 4700
Keele Street, Toronto, Ontario, Canada, M3J 1P3.

Catherine Théberge-Turmel
Department of Electrical Engineering and Computer Engineering, Université
de Sherbrooke, 2500 boul. Université, Sherbrooke (Québec) J1K 2R1, Canada.

Robert Tow
AT & T Labs, 75 Willow Road, Menlo Park, CA 94025, USA.

Iain Werry
Department of Cybernetics, University of Reading, Whiteknights,

PO Box 225, Reading, Berks RG6 6AY, United Kingdom.

R. Michael Young
Department of Computer Science, Box 8206, College of Engineering, North
Carolina State University, Raleigh, NC 27695, USA.


Chapter 1
SOCIALLY INTELLIGENT AGENTS
Creating Relationships with Computers and Robots
Kerstin Dautenhahn
1
, Alan Bond
2
, Lola Cañamero
1
, and Bruce Edmonds
3
1
University of Hertfordshire,
2
California Institute of Technology,
3
Manchester Metropolitan
University
Abstract This introduction explains the motivation to edit this book and provides an over-
view of the chapters included in this book. Main themes and common threads
that can be found across different chapters are identified that might help the
reader in navigating the book.
1. Background: Why this book?

The field of Socially Intelligent Agents (SIA) is by many perceived as a
growing and increasingly important research area that comprises very active
research activities and strongly interdisciplinary approaches. The field of So-
cially Intelligent Agents is characterized by agent systems that show human-
style social intelligence [5]. Humans live in individualized societies where
group members know each other, so do other animal species, cf. figure 1.1.
Although overlap exists, SIA systems are different from multi-agent systems
that a) are often only loosely related to human social intelligence, or use very
different models from the animal world, e.g. self-organization in social in-
sect societies, or b) might strongly focus on the engineering and optimization
aspects of the agent approach to software engineering.
In the past, two AAAI Fall Symposia were organized on the topic of So-
cially Intelligent Agents, in 1997 and 2000. Both symposia attracted a large
number of participants. The first symposium gave a general overview on the
spectrum of research in the field, and in the years following this event a vari-
ety of publications (special journal issues and books) resulted from it
1
.Also,
a number of related symposia and workshops were subsequently organized
2
.
Unlike the 1997 symposium, the 2000 symposium specifically addressed the
issue of Socially Intelligent Agents - The Human in the Loop. A special issue
2 Socially Intelligent Agents
Figure 1.1. Elephants are socially intelligent biological agents that live in family groups with
strong, long-lasting social bonds. Much research into socially intelligent artifacts is inspired by
animal (including human) social intelligence.
of IEEE Systems, Man and Cybernetics, Part A emerged from this sympo-
sium which provides an in depth treatment of a few research approaches in
that area

3
. Unlike the special journal issue, this book has a radically differ-
ent nature: it is intended to be the first definitive collection of current work
in the rapidly growing field of Socially Intelligent Agents, providing a useful
and timely reference for computer scientists, web programmers and designers,
computer users, and researchers interested in the issue of how humans relate
to computers and robots, and how these agents in return can relate to them.
Each of the 32 chapters is, compared to a journal article, relatively short and
compact, focusing on the main theoretical and practical issues involved in the
work. Each chapter gives references to other publications that can provide the
reader with further detailed information.
In the area of software and intelligent agents many other publications are
available, e.g. [1], [9], [6], proceedings of the Autonomous Agents and other
conferences, just to name a few. However, none of them provide a state-of-
the-art reference book on Socially Intelligent Agents with an interdisciplinary
approach including both software and robotic agents.
Despite many publications that either a) specialize in particular issues rele-
vant to Socially Intelligent Agents (e.g. robots, emotions, conversational skills,
narrative, social learning and imitation etc., cf. [12], [10], [3], [7], [2], [11],
[4]), or b) present a small number of in-depth discussions of particular research
projects (published in journal issues mentioned above), the field of Socially
Intelligent Agents is missing a state-of-the-art collection that can provide an
overview and reference book. More and more researchers and PhD students
Creating Relationships with Computers and Robots 3
are interested in learning about and participating in SIA research, but at present
the only way to learn about the field is to go through and select among a large
number of widely ‘distributed’ and often difficult to access publications, i.e.
journal issues, books, conference and workshop proceedings etc. Our motiva-
tion to edit this book was therefore based on the belief that there is a strong
demand for a book that can be used by students, researchers and anybody in-

terested in learning about Socially Intelligent Agents. The main strength of
the book is the breadth of research topics presented and the references given at
the end of each chapter, so that researchers who want to work in that field are
given pointers to literature and other important work not included in the book.
The book presents a coherent and structured presentation of state-of-the-art
in the field. It does not require the reader to possess any specialist knowledge
and is suitable for any student / researcher with a general background in Com-
puter Science and/or Artificial Intelligence or related fields (e.g. Cognitive
Science, Cybernetics, Adaptive Behavior, Artificial Life etc.). Also, at present
the growing field of Socially Intelligent Agents has no core text that can be
used in university courses. This book fills this gap and might be used in differ-
ent courses for postgraduate studies, and as research material for PhD students,
e.g. for studies in Applied Artificial Intelligence, Intelligent and Autonomous
Agents, Adaptive Systems, Human-Computer Interaction, or Situated, Embod-
ied AI.
2. Book Structure and Chapter Overviews
The remaining thirty-two chapters of this book are organized into two parts.
The structure of the book is visually shown in figure 1.2. The first part ad-
dresses the theory, concepts and technology of Socially Intelligent Agents. The
second part addresses current and potential applications of Socially Intelligent
Agents. The first part of the book has twelve chapters organized in three sec-
tions covering three major themes, namely relationships between agents and
humans, edited by Alan Bond, agents and emotions/personality edited by Lola
Cañamero, and communities of social agents, edited by Bruce Edmonds. The
second part of the book consists of twenty chapters organized in five sections
covering the themes of interactive therapeutic agent systems, edited by Kerstin
Dautenhahn, socially intelligent robots, edited by Lola Cañamero, interactive
education and training, edited by Kerstin Dautenhahn, social agents in games
and entertainment, edited by Alan Bond, and social agents in e-commerce,
edited by Bruce Edmonds. The content of the sections and chapters is de-

scribed in more detail below.
Note, that thematically we have strong overlaps between all chapters in this
book, the division into thematic sections is mainly of practical nature. This
4 Socially Intelligent Agents
Figure 1.2. Book structure, showing the division into two parts and eight sections. Chapter
numbers are given.
introductory chapter therefore concludes by identifying a few of these thematic
overlaps (section 3).
2.1 Agent-Human Relationships
This first section engages the reader in the question of what a relationship
between a computer agent and a human user might be. Are relationships pos-
sible at all, and if so, what would it mean for an agent and a human to have
a relationship? What theoretical bases should we use for this problem? How
Creating Relationships with Computers and Robots 5
can we design and implement agents that engage in and maintain relationships
with users? How will we be able to provide and to manage such agents?
There are a number of dimensions of analysis of this problem, such as:
What interaction methods and protocols are efficacious?
What kinds of information should be exchanged?
What knowledge can be and should be shared?
How do we model the other?
– How should a computer agent model the human?
– How will the human user model or think of the computer agent?
What kinds of constraints on behavior of both partners can result, how do
we represent them, communicate them, detect them, renegotiate them?
and
What are the effects, benefits and drawbacks of agent-human relation-
ships?
Chapter 2, written by Per Persson, Jarmo Laaksolahti, and Peter Lönnqvist
presents a social psychological view of agent-human relationships, drawing on

their backgrounds in cultural studies and film. They observe that users adopt
an intentional instead of mechanical attitude in understanding socially intelli-
gent agents, pointing out the active role of the human mind in constructing a
meaningful reality. According to their constructivist approach, socially intelli-
gent agents must be meaningful, consistent and coherent to the user. In order
to characterize this mentality, the authors draw upon a comprehensive back-
ground including folk psychology and trait theory. They advocate the use of
folk theories of intelligence in agent design, however this will be idiosyncratic
to the user and their particular culture.
In chapter 3, Alan Bond discusses an implemented computer model of a
socially intelligent agent, and its dynamics of relationships between agents and
between humans and agents. He establishes two main properties of his model
which he suggests are necessary for agent-human relationships. The first is
voluntary action and engagement: agents, and humans, must act voluntarily
and autonomously. The second is mutual control: in a relationship humans
and agents must exert some control over each other. The conciliation of these
two principles is demonstrated by his model, since agents voluntarily enter into
mutually controlling regimes.
Bruce Edmonds presents in chapter 4 a very interesting idea that might be
usable for creating socially intelligent agents. He suggests that agents be cre-
ated using a developmental loop including the human user. The idea is for
6 Socially Intelligent Agents
the agent to develop an identity which is intimately suited to interaction with
that particular human. This, according to the author may be the only way to
achieve the quality of relationship needed. In order to understand such a pro-
cess, the author draws upon current ideas of the human self and its ontogenetic
formation. He articulates a model of the construction of a self by an agent, in
interaction with users.
In chapter 5, Katherine Isbister discusses the use of nonverbal social cues
in social relationships. Spatial proximity, orientation and posture can commu-

nicate social intention and relationship, such as agreement or independence
among agents. Facial expressions and hand, head and body gestures can indi-
cate attitude and emotional response such as approval or uncertainty. Spatial
pointing and eye gaze can be used to indicate subjects of discussion. Timing,
rhythm and emphasis contribute to prosody and the management of conversa-
tional interaction. Her practical work concerns the development of interface
agents whose purpose is to facilitate human-human social interaction. She re-
ports on her experience in two projects, a helper agent and a tour guide agent.
2.2 Agents and Emotions/Personality
Emotion is key in human social activity, and the use of computers and robots
is no exception. Agents that can recognize a user’s emotions, display meaning-
ful emotional expressions, and behave in ways that are perceived as coherent,
intentional, responsive, and socially/emotionally appropriate, can make impor-
tant contributions towards achieving human-computer interaction that is more
‘natural’, believable, and enjoyable to the human partner. Endowing social ar-
tifacts with aspects of personality and emotions is relevant in a wide range of
practical contexts, in particular when (human) trust and sympathetic evaluation
are needed, as in education, therapy, decision making, or decision support, to
name only a few.
Believability, understandability, and the problem of realism are major issues
addressed in the first three chapters of this section, all of them concerned with
different aspects of how to design (social) artifacts’ emotional displays and
behavior in a way that is adapted to, and recognizable by humans. The fourth
chapter addresses the converse problem: how to build agents that are able to
recognize human emotions, in this case from vocal cues.
In chapter 6, Eva Hudlicka presents the ABAIS adaptive user interface sys-
tem, capable of recognizing and adapting to the user’s affective and belief
states. Based on an adaptive methodology designed to compensate for per-
formance biases caused by users’ affective states and active beliefs, ABAIS
provides a generic framework for exploring a variety of user affect assessment

methods and GUI adaptation strategies. The particular application discussed
in this chapter is a prototype implemented and demonstrated in the context of
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.
8 Socially Intelligent Agents
This research was followed by the development of various pieces of software—
among others, an agent capable of analyzing telephone quality speech and to
distinguish between two emotional states—‘agitation’ and ‘calm’—with good
accuracy.
2.3 Social Agent Communities
Although it has always been an important aspect of agents that they dis-
tribute computation using local reasoning, the consequences of this in terms
of the increased complexity of coordination between the agents were realized
more slowly. Thus, in recent years, there has been a move away from designing
agents as single units towards only studying and implementing them as whole
societies. For the kind of intelligence that is necessary for an individual to be
well adjusted to its society is not easy to predict without it being situated there.
Not only are there emergent societal dynamics that only occur in that context
but also the society facilitates adaptive behaviors in the individual that are not

possible on its own. In other words not only is society constructed by society
(at least partially) but also the individual’s intelligence is so built. The authors
in this section of the book are all involved in seeking to understand societies of
agents alongside the individual’s social intelligence.
In chapter 10 Juliette Rouchier uses observations of human social intelli-
gence to suggest how we might progress towards implementing a meaningful
social intelligence in agents. She criticizes both the complex designed agent
approach and the Artificial Life approach as failing to produce a social life that
is close to that of humans, in terms of creativity or exchange of abstractions.
She argues that agents will require a flexibility in communicative ability that
allows to build new ways of communicating, even with unknown entities and
are able to transfer a protocol from one social field to another. A consequence
of this is that fixed ontologies and communication protocols will be inadequate
for this task.
Hidekazu Kubota and Toyoaki Nishida (chapter 11) describe an implemented
system where a number of "artificial egos" discursively interact to create com-
munity knowledge. This is a highly innovative system where the artificial egos
can converse to form narratives which are relayed back to their human counter-
parts. The associative memory of the egos is radically different from those of
traditional agents, because the idea is that the egos concentrate on the rele-
vance of contributions rather than reasoning about the content. This structure
facilitates the emergence of community knowledge. Whether or not this style
of approach will turn out to be sufficient for the support of useful community
knowledge, this is a completely new and bold style which will doubtlessly be
highly influential on future efforts in this direction.