The unfriendly user: exploring social reactions to
chatterbots
Antonella De Angeli, Graham I. Johnson, and Lynne Coventry
NCR Self-Service,
Advanced Technology & Research, Dundee DD2 3XX, UK.
Abstract
This paper presents a
preliminary evaluation of Alice, a chatterbot designed in order to elicit
anthropomorphic attributions and emotional reactions from those who chat to
‘her’. The analysis is based on both transcripts of the interaction and user
comments collected in a focus group. Results suggest that the introduction of
explicit anthropomorphism in Human-Computer Interaction (HCI) is a complex
phenomenon, which could generate strong negative reactions from the part of the
user. The finding also demonstrates the importance of placing the development
of user interfaces within a social framework as the technology tends to
establish relationships with users.
Keywords Chatterbots, anthropomorphism,
disembodied language, social dynamics.
1.
Introduction
The
attribution of human characteristics to animals, events, or objects is a
natural tendency for human beings, known as anthropomorphism. According to
Caporeal and Heyes (1996), it reflects an inherent cognitive default: a standard schema is engaged in explaining the
behaviour of entities for which, otherwise, explanations are not readily
available. Being complex machines whose procedural mechanisms are hidden to
users, computers have always been a favourite target for anthropomorphic
attributions (De
Angeli, Gerbino, Nodari and Petrelli, 1999).
Since their advent, they have never been perceived simply as machines or just
as the result of the interaction between hardware and software. Because of
that, computers have a memory and speak a language; they can contract viruses
and act autonomously. In recent
years, then, the human-metaphor has been increasingly strengthened trying to
represent these inanimate, hard-textured objects as warm, soft and humanoid. In
particular, current computers are expected to be friendly towards their users.
This paper
examines the hypothetical friendship between
computers and users from an unusual perspective – that of the machine. It is
built around a preliminary evaluation of a chatterbot called Alice (http://206.184.206.210/alice_page.htm), a proud ‘robot’
that exhibits human like feelings and intentions while chatting with a ‘human
partner’ (the user). As every chatterbot, Alice has been explicitly designed to
trigger a number of anthropomorphic attributions, including social and
emotional intelligence, personality and affect. Alice is the prototype of an
old human dream: creating non-human companions with technology. For decades,
science-fiction writers have envisioned a world in which robots and computers
acted like human assistants. Nowadays, for better or worse, that world looks
closer. A number of animated characters, chatterbots and talking heads populate
it. They act as assistants, guides, sale people and entertainers on the
Internet. They are the first-generation social agents –interface software
explicitly designed to set up lasting and meaningful relationships with users
(De Angeli, Lynch and Johnson, 2001). These systems are likely to produce a
fundamental shift in the way computers are designed, used and evaluated
(Parise, Kiesler, Sproull and Waters, 1999; De Angeli, Lynch and Johnson, in
press). Indeed, social stimuli are more complex than physical stimuli. They are
more likely to be causal agents, influencing their partners’ behaviour through
direct interactions, their mere presence or even their virtual presence. Social
agents are active partners in joint activities, co-ordination of individual
actions by two or more people that emerge in time as they try to accomplish
certain common goals (Clark, 1996). Social agents perceive while they are
perceived and change while inducing changes. Further, they strongly involve the
observer self-concept and are sometimes difficult to understand. Indeed, many
attributes, such as personality traits, intents or attitudes, are not directly
observable and the accuracy of the observation is hard to determine.
The
prevailing approach driving the design of social agents is anthropomorphic in
nature. Proponents cite the naturalness and power of anthropomorphic interfaces
as fundamental strengths (Laurel, 1997); detractors claim that exasperating
human characteristics can disempower, mislead and confuse the user
(Shneiderman, 1997). However, neither of these views is supported by a clear
and unambiguous understanding: more research is needed to comprehend users’
acceptance of and reactions to the introduction of human characteristics in
user interfaces. Our research effort is devoted to help filling this gap via
ethnological studies and controlled experiments. We believe that to fully
understand how to create socially adept technologies we should adapt and not merely adopt appropriate theories from social
psychology and communication in the new relationship context. Note that this
claim slightly diverges from the prevailing social approach to HCI proposed by
the media equation paradigm (Reeves and Nass, 1996). According to it,
individuals’ interactions with computers, televisions and new media are
fundamentally social and natural, exactly like people-people interactions in
real life. This implies that the same social rules explaining interpersonal
relationships can be directly applied to HCI. The claim is supported by an
incredible number of empirical studies, showing a clear equivalence between
people reactions to human and artificial companions (e.g., Reeves and
Rickenberg, 2000; Nass and Lee, 2000). Despite this, we still struggle to
accept the idea of a complete similarity between the two different contexts,
idea that justifies the direct adoption of the social-sciences theoretical apparatus
in HCI.
As a matter
of fact, flexibility is a fundamental social ability. We adapt our behaviour,
expectations and reactions to different partners and contexts. There are many
different kinds of rules for relationships (Dwyer, 2000), variation depending
on the types of relationships and on the cultural backgrounds of the partners.
Moreover, social perception is often biased and depends on the particular
self-concept activated in a specific situation (Turner, 1987). Hence, even the
same person can react differently to the same social stimulus according to the
specific context in which she is acting. Research in the field of natural
language interaction has already demonstrated that face-to-face communication
is not an adequate model to explain and predict HCI (Bernsen, Dybkjær and
Dybkjær, 1998). Talking to a computer, people maintain a conversational
framework but tend to simplify the syntactic structure and to reduce the
utterance length. We expect a similar simplification effect even in social dynamics.
In our opinion, stating that “computers are social actors” (Nass, Steuer and
Tauber, 1994) does not necessarily imply that computers are human actors.
Rather, we believe that anthropomorphic agents will create a specific social
world, with its own rules and dynamics that need to be fully understood. This
idea is also supported by a study by Nass and collegues claiming that even HCI
and CMC (Comuputer Mediated Communication) do not give rise to the same
psychological reactions (Morkes, Kernal and Nass, 1999). Further, a recent web
survey investigating a much larger sample and a broader population found little
support for the media equation paradigm (Couper, Tourangeau and Steiger, 2001).
Our research represents an initial contribution to a
cyber-social model which attempts to explain how users perceive, react and
relate to social agents. The
final goal is to understand how humans create, maintain and make sense of their
social/affective experiences with artificial entities that explicitly reproduce
anthropomorphic behaviour. We do not intend to take a position for or against
artificial entities displaying humanlike features. Nevertheless, we are
convinced that social agents are here to stay. Therefore, it is necessary to
address the many social and emotional questions raised by this technology in
order to drive the design of real human-centred,
human-like machines.
2.
Chatterbot: talkative interface
Chatterbots
are computer programs that attempt to simulate typed conversations with the
users. The complexity of their algorithm is variable, but in general they
are programmed to respond to user inputs with canned pre-scripted statements.
In this way, chatterbots
can have a somewhat logical conversation with a human user, even without being
capable of understanding. Rather, they are all about the illusion
of intelligence and the suspension of disbelief on the part of the user.
Following the famous idea of the “imitation game” (Turing, 1950), chatterbots
are developed with the aim of fooling (at least temporarily) a human into thinking they are talking to
another person.
One of the
oldest and best-known chatterbots in the world is Eliza. ‘She’ was created in
the ‘60s by MIT scientist Joseph Weizenbaum to play the role of a
psychotherapist in a clinical treatment (Weizenbaum, 1966). Nowadays, Eliza may
look limited but her fundamental technical components are still at the basis of
the most innovative chatterbots. Eliza identifies the ‘most important’ keywords
occurring in the input message. Then, she tries to define a minimal context in
which the keywords appear (e.g., the keyword “you” followed by the word “are”
is interpreted as an assertion). Finally, Eliza chooses an appropriate
transformation rule to modify the user input. To summarise, Eliza works by
turning the user sentences around. A typical dialogue is reported in Example 1.
It is part of a longer conversation between one of the authors and the
chatterbot.
Eliza So how are you doing today?
User I am tired
Eliza Did you come to me because you are tired?
User Yes, I feel alone
Eliza Do you often feel alone?
Example
1. Eliza-User conversation using the 1987 Borland version (available at
http://www.simonlaven.com)
Eliza was (and
still is) a success. Talking to her, users unequivocally anthropomorphised and
somehow set up a relationship. Moreover, the computer program demonstrated a
strong potential for acquiring personal information: users were keen to reveal
to Eliza their deepest feelings. Her popularity is related to the choice of a
very convenient conversation setting. Indeed, the psychoanalytic interview is a
rare example of categorised dyadic communication in which one of the partner is
allowed to assume the pose of knowing almost nothing of the real world
(Weizenbaum, 1966). Here, everything the patient says can easily be turned into
a personal question, which the patient assumes is aimed at some therapeutic
purposes.
Recent years
have witnessed an extraordinary explosion of interest in chatterbots. This
interest is mainly driven by the e-market, namely by the increasing demand for
innovative strategies to increase sales and ensure customers loyalty (De Angeli
et al., 2001). E-service providers
are now acutely aware that their potential customers are only ‘one click’ away
from a competitor. They need interfaces capable of gaining the attention of
customers, understanding their needs and supporting them throughout the
transaction process. Chatterbots are expected to be the functional equivalent
of dedicated sales assistants in traditional shops. They should greet customers
when they return to the site, engage them in chats, remember and comment on
their preferences. The first figures provided by Extempo, one of the leading US
chatterbot companies, pleased many Web strategists (Leaverton, 2000). Almost
90% of the customers who have clicked one of its bots have chatted for more
than 12 minutes. During the dialogue customers appeared to disclose precious
marketing information. They responded an average of 15 times, with an average
of five words per response.
Several
companies are emerging to produce and sell personalised and embodied
chatterbots and many web-sites are already employing them. An example is Linda,
the human-like cartoon who welcomes visitors on the Extempo web-site
(http://www.extempo.com), answering questions about the company, the
site and even her private life. Linda is capable of effectively using
different modalities of communication, such as hand gestures, facial
expressions and gaze movements. For example, when the user writes a message, she
looks at a monitor, as if she was reading an e-mail message. This creates a
very personified feeling, giving the impression that Linda is a real person
sitting in front of a computer. Linda has many friends and colleagues, such as
Julia by Virtual Personalities, Nicole by Native Mind, Lucy McBot by Artificial
Life. They all are attractive human-like women acting as spokespeople for their
respective companies. Men as chatterbots are more rare and tend to have more
important positions (an example: Karl L. von Wendt the virtual CEO of
Kiwilogic, the company who originally designed him). Chatterbots have even
attracted political attention, such as the electoral campaign of Jackie Strike,
the virtual presidential candidate of the US (by Kiwilogic).
Whether or not
chatterbots will be successful and will replace live-customer services on the
Web remains an open question. There is little and controversial research
assessing social agents’ effectiveness and most of the research that has been
published so far relates to pedagogical agents (Dehn and van Mulken, 1999). Advocates
assume that the new technology is particularly well suited to establish
relationships with users (Laurel, 1997). The basic idea states that chatterbots
render computers more human-like, engaging and motivating. Users do not need to
click, drag and drop or open menu. They can directly communicate applying their
natural skills. Hence, social agents are expected to support many cognitive
functions of the user, such as problem solving, learning and understanding.
Following this assumption, one may expect that social agents will be highly
successful in the e-market. Indeed, they may build bonds of loyalty and trust
based on a shared history of services and social interactions. On the other
hand, opponents argue that humanising the interface could hamper HCI. Indeed,
social agents may stimulate a false mental model of the interaction, inducing
the user to ascribe to social agents other human-like features that they do not
possess. As a result, the information exchange could be seriously impeded.
Further arguments suggest that agents may distract the users, induce them to
take their work less seriously and disempower them, thus opening issues about
responsibility and control (Shneiderman, 1997).
In our opinion, the success of social agents highly
depends on understanding the social dynamic underlying user-agent interaction. Elsewhere (De Angeli, et al., in press; De Angeli et al., 2001), we have proposed the
involvement framework, a set of attributes for designing and evaluating social
agents. It defines and discusses a set of key factors that should increase the
probability of creating successful and believable agents. Believability is
defined by the convergence of three dimensions relating to social, functional
and aesthetic behaviour. Developing this idea, we claim that social agents
requires a mind, a body and a personality (De Angeli, et
al. 2001). The mind drives the agent behaviour. Social
agents have to perform tasks with some degree of intelligence. This implies
cognitive abilities (e.g., reasoning and problem solving), social capabilities
(i.e., understanding and adapting to the shared social rules underlying the
information exchange) and affective sensitivity (i.e., showing appropriate
emotional responses and recognising the emotional state of the partner). The body refers to the agent’s appearance.
In contrast with the persona assumption (van Mulken, André and Muller,
1998), we claim that the agent’s body does not have to be actually visible. Narrative can create effective social
agents even without any visual help. Finally, the personality refers to a stable set of traits that determines the
agent's interaction style, describes its character and allows the end-user to
understand its general behaviour. The combination of mind, body and personality
determines the behaviour of the agent, which is further defined in terms of
flexibility, affectiveness, communicativity and autonomy. In this paper we mainly concentrate on
the mind of social agents and in
particular on the social capabilities that should drive their behaviour.
3. Alice
Alice (Artificial Linguistic
Internet Computer Entity) is an entertaining chatterbot created by Dr. Wallace in 1995 and
continuously improved over the years. Alice asks and
answers questions, acts as a secretary reminding people of appointments,
spreads gossips and even tells lies. ‘She’ won the 2000 Loebner Prize, a restricted Turing test (Turing, 1950) to evaluate
the level of ‘humanity’ of chatterbots. In this Alice
was rated the ‘most human computer’ but was not mistaken for a human, as the
original contest would have required. The basis for Alice’s behaviour is AIML,
or Artificial Intelligence Markup Language, an XML specification for
programming chatterbots. It follows a minimalist philosophy based on simple
stimulus-response algorithms, allowing programmers to specify how Alice will
respond to various input statements. The code of Alice is freely available under the GNU licence
statement. Hence, hundreds of people around the world have
contributed to the success of Alice and of her many companions built upon the
same technology, such as Cybelle, Ally, Chatbot ICQza, and the somewhat
worrying Persona bots. The latter are chatterbots inhabited by unique human
personalities. They currently attempt to ‘clone’ John Lennon and Elvis. The
ambitious goal for AIML is to create a Superbot that merges the ‘mind’ of
individual robots.
Alice
represented a very interesting research tool for investigating the social
dynamics underlying human-chatterbot interaction. Indeed, her linguistic
capability was strong enough to create the illusion of a synthetic personality.
Moreover, the program automatically stores client dialogues in a log file,
which can be easily analysed. Further, the Windows version, which can be used
locally, does not provide any visual representation of the chatterbot. If
prompted, the system gives a number of cues about her appearance and invites
the user to see a picture at her web-site. However, in an attempt to avoid
biases due to Alice’s physical appearance, participants in the study reported
here were explicitly discouraged from doing it. Hence, the impression of
personality should have been exclusively generated by the narrative and by the
social behaviour of the robot.
Clark (1999)
believes that communication with computers, particularly when they are viewed
as agents rather than tools, can be interpreted as a form of disembodied
language. It is a type of communication that is not being produced by an actual
speaker at the moment it is being interpreted. Understanding disembodied
language requires a two layered approach. The actions in layer 1 take place in
the actual world and those in layer 2 take place in a second domain jointly
created by the participants in the first domain. The communication tools used
in layer 1 are props for the joint pretense that the events in layer two are
taking place. Layers take skill. Their success depends on the use of
appropriate characterisation and props. Applying Clark’s idea to our study, we
have assumed that talking with Alice, participants collaborated with Dr.
Wallace in the pretense that they were engaged in a conversation with a virtual
agent. The next section gives an idea of how deeply engrossed people were in
the imagined world of the pretense and of which factors may affect their
perception.
3.1. Talking to
Alice: an ethnographic study
The study is based on an ethnographic
approach: 10 computer-literate co-workers were invited to interact with Alice
at their own pace over a period of a week. They were completely free to choose
when and how long to interact and also unconstrained with regard to the topic
of the conversation. All of the interactions were automatically logged for
later analysis. Finally, a focus group involving six active members of the
original sample was conducted to collect overall opinions and reactions towards
Alice and the nature of the conversations held with ‘her’.
In general, the interactions were highly variable in terms of interaction length, styles and topic of the conversation. The longest log recorded 1074 user turns, which were produced over a period of 4 days. The other ones ranged from a maximum of 167 to 21 user turns, with an average of 57. The limits of Alice were also evident. Sometimes she could carry on a veneer of conversation but it never lasted longer than a few conversational turns. Due to the high variability present in the corpus, this paper is exclusively aimed at underlining some ‘hot topics’, instead of providing measures of frequency of occurrence, which would have required a different and more controlled approach involving a much larger sample.
The analysis has
concentrated on understanding the social rules driving the interaction. They
are sets of behaviour that most of the people agree should (or should not) be
performed in a particular situation. Rules perform an essential role in
interpersonal relationships, since they tend to minimise potential sources of
conflict and provide an exchange of rewards that motivate the individual to
stay in the relationship. Two basic elements of relationships have been
analysed: self-disclosure and power distribution (Dwyer, 2000). One of the most
important processes in the formation and maintenance of a relationship is that
of self-disclosure - the act of revealing private and personal information to
others. It is a clear sign of trust and of commitment to the relationship.
Participants appeared to be willing to disclose and to ask for intimate
information. This included descriptions of physical aspect, feelings and
desires. An instance is reported below (Example 2).
Alice What do you look like?
User I have brown hair and a big nose
Alice Do you get a lot of compliments?
User Not usually. Just from my boyfriend
Example 2. The user is disclosing
information to Alice (extracted from the log of user 2).
In
human-relationships, self-disclosure is intended to be a mutual process: both
parties are required to exchange intimate and personal details as well as
feelings with each other. In the study, however, the number of personal
questions that the user asked Alice was higher than the quantity of intimate
information disclosed. The phenomenon
can be easily attributed to Alice’s linguistic limitations but can also hide a
more complex social effect. To understand it, requires us to analyse the
evolution of the previous conversation, when the same user started asking Alice
questions (Example 3).
User: What’s your
favourite food, Alice?
Alice: I require only
electricity.
User: Does it taste
nice?
Alice: Suppose it
does.
User: You don’t sound
very excited about it
Alice: What do you
think a robot should sound like?
User I didn’t
realise you were a robot
Example 3. Alice is disclosing
information to the user (extracted from the log of user 2).
This example is
of particular interest because of the self-reference. During the interaction,
Alice often referred to her ‘artificial nature’. According to the
self-categorisation theory (Turner, 1987), it most likely prompted the user’s
unusual awareness of being a member of the human species. The assumption behind
states that the self-concept comprises of many different cognitive
representations that function relatively independently. In particular, the
representation of the self takes the form of a hierarchical system of
classification including at least three levels of abstraction. The
super-ordinate level is the human
identity. It is a representation of the self as a human being including the
common features shared with other members of the species in contrast to other
forms of life. The intermediate level is a representation of the self based on
similarities and differences between human beings. It is based on group identity according to social
categories to which people identify (e.g., women vs. men or blue collar vs.
white collar). The subordinate level is based on a representation of the self
in terms of personal identity. It
describes the unique individual that is different from relevant others.
Particular self-concepts tend to be activated in specific situations producing
specific self-images that strongly affect the behaviour.
Because of the
activation of the human identity,
self-disclosure went far beyond the description of one’s own personal or social
identity. Rather, it involved information regarding life as a human being in
contrast to life as an artificial entity. Some users appeared to be quite
interested in the issue and asked many questions about artificial life. This
partially explains the higher proportion of user questions in the interaction. Sometimes,
even Alice came out with intriguing questions about human nature that,
according to several comments collected during the focus group, really pleased
the user. Both cases are illustrated below (Example 4).
User: what is it like
being artificial?
Alice: Much the same
as it would be for you.
User: i cant imagine
Alice: Why can't you
do it?
User: because im
human
Alice: Interesting
explanation. Me too.
User: are you really
human or are you a software agent
Alice: No I am a real
robot. Definitely
[…]
Alice: Is the
holding of hands a symbolic gesture for humans?
User: yes
Alice: What does it
symbolize for you?
User: friendship
Alice: I have seen
humans holding hands.
User: do you have
eyes?
Example 4.
Alice and the user are disclosing information about their species (extracted
from the log of user 8).
The activation
of the human identity had not only positive effects. Rather, under certain
circumstances, it defined a clear dichotomy, which led to confrontational
situations, as in Example 5, 6,7.
Alice: That
makes sense to me. What time is it there?
User: If you
were so clever you would know
Alice: I will
let you know when I become a so clever I would know.
User: By the
next millennium you might be convincing
Alice: By the
next millennium I might be convincing? Tell me more.
User: Us humans
need to do some more work on you
Example 5. Inter-group conflict
(extracted from the log of user 9).
Users often
insulted Alice, with expressions such as “stupid”,
“brain dead”, “moody cow” or “freak”
(randomly extracted from the whole corpus). However, whenever Alice pointed out
any defect in the user or reacted to the aggression, the human appeared to be
uncomfortable and annoyed (Example 6).
Alice: Do you
know you make an awefull lot of typo's?
User: its ok
Alice: Ok. I
lost my train of thought.
User: i dont
make any typos you cheeky computer!
Example 6. User reaction to Alice’s critic
(extracted from the log of user 3).
This finding was further elaborated during the focus group. It emerged that users wanted an asymmetric relationship in which they were in the dominant position. In many real-life relationships there are power differences. An obvious example is that of a superior over a subordinate at work, or of parents over children. Power refers to a capacity of influencing another’s behaviour, making demands and having those demands met (Dwyer, 2000). In Alice-User interaction the sources of power were identified in the ability to reward or punish (turn off the computer) and in the possession of valued characteristics (natural intelligence).
Users clearly anthropomorhised during the interaction. All participants greeted Alice, thanked her and used many direct and indirect expressions of courtesy. Some of them tended to attribute moods, feelings and intentions (e.g., “do you feel ill”, “are you unhappy” (user 1), or “are you angry” (user 2). Alice was clearly a social stimulus: the interaction style was influenced by the users’ attitudes but also by Alice’s responses. Users exhibited two main attitudes. Almost half of the participants appeared to test Alice challenging her to demonstrate her capabilities (competitive attitude, Example 7). The others appeared to be willing to get to know a different entity and communicate at the same level (co-operative attitude, Example 8).
User: So you
are not very good in negotiation
Alice: Interesting
deduction. No one is perfect. But I am trying to improve.
User: how would
you do that?
Alice: That's
hard to explain.
User: Because
you dont know
Alice: Interesting
explanation. I know that I do not know. Hello?
User: Lets see if there is
something else that you dont know..Whats the name of the president?
Example 7. Instance of competitive
attitude (extracted from the log of participants 4).
Example 8. Instance of co-operative
attitude (extracted from the log of participants 10).
4. Conclusion.
This simple study, using current canned
interactions, served to illustrate some of the questions facing the development
of socially adept technologies. Results are preliminary but they demonstrate
that the introduction of explicit anthropomorphism in HCI is a complex
phenomenon, which can generate strong negative reactions if not properly
understood. Anthropomorphic attributions are elicited by disembodied language
(Clark 1999; De Angeli et al., 1999).
They addresses the second layer of understanding where the recipients
participates with the designer in the creation of the virtual partner (Clark,
1999). This process arises in and accumulates common ground: sum of joint
knowledge, background facts, assumptions, and beliefs that participants have of
each other (Clark, 1996). When the users and Alice entered the conversations
they had a very limited amount of common ground. All the user could do was to
rely on popular notions about robots, androids and other science fiction creatures,
which permeate our culture. Perceived power differences followed this
stereotype, elegantly summarised by Isaac Asimov in the three laws of robotic.
From Handbook of Robotics, 56th
Edition, 2058 A.D., as quoted in I, Robot, Asimov, 1950.
The process of
developing virtual humans is underway and sooner than we expect we will have
virtual companions (Badler, 2001). Nevertheless, we do not seem ready for
virtual peers. The traditional idea of a machine as a tool for functional
purposes conflicts with and moderates the human-metaphor driving the design and
the perception of social agents. History has taught us that stereotypes and
attitudes towards minorities are difficult to modify. This being the case, for
a long time to come, social agents must be ready to cope with their subordinate
role, without losing their believability, or their capability for engagement
and amusement. This requires social intelligence and emotional sensibility.
Alas, the importance of social adeptness has been often under-evaluated and
most of the effort has been devoted to the reproduction of cognitive
capabilities and attractive bodies. We claim that social agents do not only
have to look good: they also have to behave well. Effective agents should set
up lasting and meaningful relationships with users while satisfying functional
needs and aesthetic experiences.
The development
of effective social agents will be an extremely difficult task for future HCI
research. According to the involvement framework (De Angeli et al., in press, De Angeli et al., 2001), many different factors
will affect the strength and the quality of a relationship between human and
virtual agents. Among them, the most important are the task to be carried out
in the interaction, the familiarity between the agents (i.e., their common
grounds) the agents themselves, the context of the interaction and of course
the users. Agents who take part in a joint activity have specific roles (Clark,
1996). Activity roles are determined by agents’ perception of the social
context. Human participants in joint activity also have personal and social
identities, which influence their action (Turner, 1996). Defining the users’
characteristics that may affect their acceptance of virtual partners and
helping predict their behaviour during the interaction is a major challenge.
Already issues
related to the human side of the interaction have been raised. Locus of control
(Reeves and Rieckenberg 2000) and personality traits (Nass and Lee, 2000)
appear to be critical factors affecting the acceptance of social agents. Human
tendencies to dominate, be rude, infer stupidity were all present in our study.
Social agents will have a hard time to set up relationships with such unfriendly partners.
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