TelMeA: An Asynchronous Community Systemwith Avatar-like Agents
Toru Takahashi†, ‡ & Hideaki Takeda*, ††Graduate School of Information Science,
Nara Institute of Science and Technology,
8916-5 Takayama-cho, Ikoma-city, Nara 630-0101, JAPAN‡ATR Media Integration & Communications Research Laboratories,
2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0228, JAPAN*

National Institute of Informatics,
2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, JAPANtooru-t@mic.atr.co.jp

takeda@nii.ac.jp

Abstract: In this paper, we propose an asynchronous community system named TelMeA, which employs avatarlike agents, or scriptable animated agents, as the conversation interface. Few of current online community
systems are close to real communities both in reality and in functions. We show with the proposed system thatonline community can be more like our real community in awareness of participants and of community itself.
TelMeA realizes the following functions for asynchronous communication using avatar-like agents: individual
embodied presence, physical communicative cues, interpersonal spaces, and cooperative reference. Participants
can easily identify other participants with avatar-like agents. And people can understand meanings of otherparticipants’ messages well and become expressive in their messages with non-verbal behaviours of agents. We
performed a preliminary psychological experiment to clarify functions of avatar-like agents, and implemented theresult in TelMeA. We also evaluated TelMeA by a test use and showed that TelMeA is natural and useful forasynchronous communities.
Keywords: CSCW, CMC, Online Community, Anthropomorphized Interface, Avatar-like Agent, Awareness1 Introduction
1.1

Online Communities as Social
Information Systems

Recently, the explosion of communities on theInternet has drawn the public’s attention. CMC(Computer Mediated Communication) systems, suchas mailing lists, Usenet newsgroups, BBS (Bulletin
Board System), IRC (Internet Relay Chat), andMUD (Multi-User Dungeon) (Damer, 1998), allow
distant people to voluntarily build and gather into
communities. These communities on the Internet are
called online communities.

In many cases, participants of an online
community hold shared interests or intentions.
Furthermore, many of these participants have wide
experience and knowledge in their shared topics orproblems and various viewpoints toward subjects of
conversation.
Since
participantsofthese
communities have such extensive experience andknowledge, the communities themselves can be seen
as an information source of experience andknowledge. In these communities, especially inasynchronous ones, participants’ experience andknowledge are built with members’ notions through aprocess of describing. The built messages are shared
among the community, evaluated by each other, andarchived as conversation logs.

We believe this voluntary activity of online
communities is a new and interesting approach to
information processing. From the viewpoint of
information processing, an online community can be
regarded as a social information system that has fourfunctions:1) Information Builders:Communications
causesinformation.
Participants’ concepts are realized as storable
representations through communication in thecommunity.
2) Information Filter:
Information related to the special themes or
purposes is collected and distributed.
3) Information Editor:
Information is analysed, evaluated, andredressed to compose new information on thespecial themes or purposes.
4) Information Archive:
Information is stored and served as abundant
information resources for specialized fields.
Although these functions are promising to build
social information systems, the current online
community systems are insufficient for such a
purpose. The main reason is that the current systems
are unnatural as community systems because they aresimply designed with traditional communication
technologies, i.e., most of them are text-based
communication systems.

1.2

Anthropomorphized Interface for
Asynchronous Community System

An asynchronous community, that is an online
community based on asynchronous communication
channels such as ML, BBS, and Usenet newsgroups,
gives participants time to read others’ contributions
carefully, to retrieve information about the current
topics, and to improve their writing as they compose
messages. Hence, asynchronous communities havegreater abilities to integrate information thansynchronous communities such as text-based IRC,
MUD, and videoconference systems, because
information is more deliberated and more organized.
However, the contributions of current
asynchronous community systems are usually
represented by text-based documents. The text-based
conversation is conventional and familiar, but lacks
various implicit contexts such as awareness ofparticipants that found in ordinary face-to-faceconversations. Such a context is important especiallyin asynchronous many-to-many conversations
because text-based conversations often cause
unexpected situations like “flaming” discussions.
Identity, subjectivity, narrative, and coherence ofutterance are based on consistency in corporeality.
We think that a lack of consistency in humancorporeality causes this lack of awareness in textbased conversations.
In this paper, we propose a community system
named TelMeA, which supports activities inasynchronous communities, especially the process ofknowing each other. In order to support this process,
TelMeA employs a conversational interface withcorporeal anthropomorphized aspect. We call suchanthropomorphised conversational interface asavatar-like agent. An avatar-like agent is defined inthis paper as a scriptable anthropomorphizedanimated agent for a telecommunication-specificconversational interface. The avatar-like agent
becomes a substitute for its user in online
communities; users communicate with one anotherthrough their associated avatar-like agents.
Each avatar-like agent has its own body andindividual aspects so that users can identify eachother in TelMeA communities. A user of TelMeA
can use conversational field such as interpersonal
space among avatar-like agents, so that he or she canbe aware of other participants and their activities aswell as showing his or her own presence.
Furthermore, a user can refer to web pages forbackground contexts of conversation topics and iscapable of representing explicit and implicit contexts
for community ambience. Therefore, users can send
and receive information with rich semantics.

1.3

Organization of this Paper

This paper is organized as follows. In Chapter 2, weshow a psychological experiment to clarify functionsof avatar-like agents. We show the overview of our
prototype system for an asynchronous community,
TelMeA, in Chapter 3, and describe its details in
Chapter 4. Results of a test use of TelMeA are
shown in Chapter 5. Finally, we conclude this paperin Chapter 6.

2 Functions of Avatar-like AgentsIn this chapter, we discuss difference between
textually mediated and avatar-like agent mediatedconversation. We analysed the difference through a
psychological experiment.

2.1

ProblemsinConversations

Text-based

In face-to-face conversations, people express
themselves with physical non-verbal cues such as
facial expressions, postures, gestures, gazes, finger
pointing, and so on. People use these non-verbal

cues in a way that is complementary to vocal speechin order to convey messages quickly, clearly,
effectively and safely. For example, suppose aperson wants to give someone a message related toan object near her. She may smile and simply say
pronouns like “this” or “that” while turning her gazeand pointing her finger to the object. This expression
is not prolix but instead economical. If she represents
the same message only by text, the description may
be long-winded and tedious.
Text-based conversations can hardly express
physical cues like smiling, gazing, or pointing.
People are therefore often forced to use complicatedphrasings to avoid misunderstanding emotional
expressions or referred terms. Such complicatedphrasing can cause much trouble for both writers andreaders, and can make asynchronous conversation
sequences awkward.
Moreover, identifying people is not easy in textbased communication. New visitors often feeldifficulty to identify the participants of text-based
online communities because the existence of aparticipant is represented only by her name andmessages. Accordingly, a person’s presence is notembodied, and her identity is not recognized in a
corporeal aspect.
Figure 1 is a model of the process for recognizing
and naming a person (Young et al, 1985). The
important point of the model is that the process isstrictly sequential: a person cannot accessinformation related to other people unless familiar
aspects can be found, and she does not access
additional information on them such as names, unless
she can access their “semantic” information such as
their biographies, interests, personalities, episodes,
and so on.
By applying this model to text-based
asynchronous conversations, users cannot provide
enough information to identify people and theirnames because the participants’ names and texts areprovided without their physical aspects. Therefore, if
a visitor to the community cannot know otherparticipants well, the visitor might be able tounderstand each message of the contributions but
would probably encounter difficulty to identify aparticipant as the contributor of previous messages.
This difficulty prevents participants fromrecognizing other people’s personalities andrelationships in the group, the meta-structures inconversations, and as a result the participation of
new visitors in ongoing conversations. If messages
are provided with physical aspects in asynchronous
communities, visitors might identify and recognize

encounter a person “X”

X is unrecognized

Recognition Unit

X is known(physical aspects, voice)

Personal Identity Node(semantic information)

Additional Information(name)

X is identified

X is named

Figure 1: Model of the process for recognizing andnaming a person.

other participants more naturally and feel more freely
to join a conversation

2.2

Anthropomorphized Agent as a
Social Existence

Based on the Media Equation paradigm (Reeves et al,1994), social behaviour of anthropomorphizedagents can induce a user’s recognition in which theagent is regarded as an actual social existence, andthe user unconsciously tends to behave socially as if
she or he would toward a human (Takeuchi et al,
1999; Takahashi et al, 2000). As well as the case ofanthropomorphized agents, we think avatar-likeagents also make users recognize the agents as social
entities, which have their own subjectivities ordelegated ones.

2.3

Psychological Experiment

To verify the effects of avatar-like agents’subjectivity, we made a psychological experiment bycomparing the situation of textually mediatedconversation and avatar-like agent mediatedconversation.
First, we divided subjects into two groups andshown one group a conversation log by text andother with avatar-like agent. We told the subjects“the conversation shown here is a log that took place
via a network” before the experiment. In theconversation, three participants talked about dinner

2-a (left): Auto scrolling text style conversation
2-b (right): Conversation with physical cues by avatarlike agents

Figure 2: Screenshots of conversation under eachexperimental condition.

menu. One of them suggested Turkish food and theother suggested Russian food. Conditions of log stylefor two groups are as follows:
Text Condition:
A text style conversation by three people takes place.
All messages are given with the speaker’s names andfurthermore showed with different colours. We showed
the messages scrolled down automatically for about
130 seconds (Fig.2-a).
Agent Condition:
Three avatar-like agents appear with their owner’s
names. The avatar-like agents make conversations
using synthesized voices and balloon text for about 150
seconds. The agents have gestures, facial expressions,and gaze in accordance with the context of theconversation (Fig.2-b).

We gave a questionnaire to all subjects after theconversation. In the questionnaire, the subjects wererequested to answer questions from three choices andalso to rate attitudes on a scale of 1 to 7, where 7 is
the most positive attitude.
The questionnaire contained 1) four questions on
recalling memories about the content of theconversation, 2) three questions on identifying theutterers, 3) three questions on ratings for impressionof conversation, and 4) two questions on ratings forwhether the subject wants to participate theconversation.

2.4

Result of Questionnaire

The subjects consist of 13 men and women in their
twenties. Through their personal data, we know thatall subjects have the experience of participating insome kind of mailing list, 11 of them participating on
BBS, and 8 of them participating on chatting on theInternet. Each experiment took about ten minutes.
We placed 6 people in the Text Condition and 7
people in the Agent Condition.
Table 1 shows the results of the questionnaire.
The first row of Table 1 shows that no statisticaldifference appeared according to the rate of recalling
memories about the content of the conversations
between two groups. In contrast, the second row
shows a significant difference between subjects in
the Text Condition and those in the Agent Condition.
The identification rate in the Agent Condition was
higher than in the Text Condition (p< .05). Thisdifference is consistent with Young’s identification
model (Fig. 1). That is, physical aspects of others areimportant for identification.
In actual online communities, participants seem
to identify each other accurately. However,participants of text-based communities, especially
newcomers, incur much effort for identification ofeach utterer. This effort seems to be an obstacle to

Number of rightanswers (out of 4);
conversation recallingNumber of rightanswers (out of 3);
utterer identification
Self rating; impressionof conversation
Self rating; volunteer
for conversation

Text
Condition

Agent
Condition

2.84 (0.76)

3.16 (0.68)

No statistical difference
1.32 (0.81)
2.43 (0.78)
Significant difference(p < .05)
5.4 (0.9)
5.5 (1.9)
No statistical difference.
3.3 (1.4)

5.1 (2.1)

Tendency of statisticaldifference (p < .10)
(SD values in parentheses)

Table 1: Result of the questionnaire

community perspective and hinders spectators fromuttering. In fact, though the third row of Table 1
shows that no statistical difference is observed
according to the result of self-rating about the
impression of the conversation, the last row shows asignificant tendency of self-rating about thevoluntariness of participation in the conversation
between the Text and the Agent Condition. Subjects
with avatar-like agents showed interest to join theconversation more than subjects with text.

2.5

Conclusion of the psychologicalexperiment

The result of the experiment shows that avatar-likeagents have effects for identification of participantsin online. In addition, it was found that avatar-likeagents tend to give an incentive to participate in the
online conversation more than in the case of text.
Though part of this tendency arises from the noveltyof the avatar-like agent mediated conversation, we
think the biggest cause is the visibility of
conversation.

3 Asynchronous Community
System with Avatar-like AgentAs mentioned above, two issues in terms ofawareness should be stressed for text-based
asynchronous conversations. One is the issue of nonverbal cues and the other is the issue of identity.
Installation of avatar-like agents into asynchronous
community systems is expected to solve these twoissues to some extent.
With avatar-like agents, we can easily represent
non-verbal physical cues such as facial expressions,

F)
G)

Well, the origins of
this church is …

Express…

Express

A TelMeA Community
with AvatarAvatar-like Agents

Figure 3: Conceptual diagram of our community

gestures, and finger pointing and moving on thedisplay relative to other agents’ positions. By meansof such physical cues with avatar-like agents, users
can converse in a form close to natural face-to-facemulti-modal
communications
even
in
an
asynchronous way. In addition, avatar-like agentscan show embodied aspects associated withparticipants in asynchronous communities. Not by
participant’s names but by their agents’ aspects, newvisitors to the community can identify participants
easily and find familiarity with them throughintuition. The visitors can easily attribute contexts ofcontributions as participant’s semantic information
via avatar-like agents’ aspects. Additionally, social
presence and behaviour of the avatar-like agents cangenerate a realistic ambience in conversations in theasynchronous community.
For these effects, we developed a community
system named TelMeA, which employs avatar-likeagents as an asynchronous conversational interface.
In a TelMeA community, all contributions are in theform of scripts for avatar-like agents and the scripts
appear as behaviour of avatar-like agents, i.e.,
avatar-like agents perform face-to-face multi-modal
conversations instead of text messages.
Figure 3 shows a conceptual diagram of ouronline community. In this community, users are able
to become aware of the presence of otherparticipants by the displays of their avatar-like agents.
However, such presence does not mean thesynchronous access of associating participants.
Rather the presence of an avatar-like agent stands for
track of the activity of a participant and this presence
provides an ambience of reality and dynamics to theasynchronous conversation.
By means of TelMeA, users can script theiragents with the following types of representations:A) Voice by text-to-speech synthesizerB) Text writings in balloons
C) Facial expressions by animationsD) Gestures by animation
E) Face rounding by animation

Finger pointing by animation
Moving on the display in connection withanimation
In addition to the above, users can do thefollowing without the agents:
H) Specify URLs of any web pages and open
the pagesI) Upload any image files to share and openthem
By combining these types of representation, users
can perform a variety of expressions:Utter with a laughRespond with head-nodding
Gesticulate with/without voice
Move to an agent and address it
Open and compare two web pages
Point a finger at an image on a page
Show and comment on a picture

4 Implementation of TelMeA
WWW is a sociable place (Donath et al, 1994).
TelMeA makes the WWW more sociable. TelMeA
was developed as a web application system, andusers can access information via web browsers. The
server is implemented with JSP (JavaServer Pages)
technologyi), and avatar-like agents are in the entirely
client-side program implemented by MS Agenttechnologyii). MS Agent is a set of software agentsfunctioning as interactive personalities, and it can be
programmed with JavaScript code in web pages(Bell et al, 1997; Microsoft, 1998). Since MS Agentis used, each client is needed for installingcomponents of MS Agent as well as its character
files as the avatar-like agent. Internet Explorer isrequired for the client browser on TelMeA because
MS Agent uses ActiveX technologyiii).
Figure 4 shows the architecture of the TelMeA
system. One TelMeA system manages multiple
communities and multiple users who can belong tomultiple communities at the same time. The databasein the server stores the attributes of communities andusers. All of the contributions in the communities arealso registered in the database in the form of
conversation sequences. The contributions aredescribed in an original script language named
ALAScript. A description of ALAScript consists oftexts with tags associated with agent behaviours. Thei)

JavaServer Pages Technology[http://java.sun.com/products/jsp/]
ii)
Web Workshop – Microsoft Agent Home –[http://www.microsoft.com/msagent/]iii)
ActiveX Controls[http://www.microsoft.com/com/tech/ActiveX.asp]

Server (Java Servlet)
WWW
DB• Participants• Communities
ALAScripts
Web Relay
ServerService Interface

Client (Internet Explorer + MS Agent)submitFigure 5: Authoring page of TelMeA
Control Frame

request

ALAS-Interp

User’s Agent
Conversation List

Figure 4: Architecture of TelMeA

set of tags used in the current ALAScript are as
follows.
Identification<#actor> agent_name
Designation of the active avatar-like agent
Verbal Representation<#speak> comment_sentence
Utterance with synthesized voice and text inballoons<#think> comment_sentence
Balloon utterance without voice
Physical Representation<#play> animation_namePhysical behavior by animation<#move> point_on_the_screen
Movement to a designated point on the screen
Interpersonal Space<#approach> agent_nameApproach toward a designated avatar-like agent
Cooperative Reference<#open> url
Denotation of a designated web page<#refer> image_id @ url
Denotation and Reference of a designated image
file uploaded by a participant or located on adesignated web page
Figure 5 shows the authoring page. The
contributions written in ALAScript are composed on
this authoring page. A user can compose descriptions
by checking her agent’s actual behaviours. The user
is also able to perform a preview through all acts ofher agent to determine whether the agent is behaving
as she wants. Because TelMeA is not an avatarbased virtual society system but an asynchronous
community system, the user is able to modify her

Shared Web Page
ALAS-Interp

Figure 6: Configuration of a community web page

message with the preview of the message until she isfully satisfied.
Figure 6 is a screenshot of a community’s web
page. When a user enters a community, a web pagein this figure appears and then the agent associatedwith the user appears. The TelMeA community’s
web page consists of three frames. The control frame
appears at the head of the page, which shows several
icons to control the system. The main frame shows
the web page, which is shared as a backgroundcontext of the conversation. Initially, this frameshows the welcome page that is registered when thecommunity is created. The right-hand side frameshows a list of conversations already done in thecommunity. If the user clicks any conversation title,
all avatar-like agents participating in thisconversation appear and start to talk (Fig. 7).

Figure 7: Conversation scene among agents(A “penguin” agent is explaining a figure while
pointing at it)a) “speak”, “think”, or “play” tag
MS Agent
Control(1)

Web Browser

ALAS-Interp(1) request for executionb) “approach” tag
MS Agent
Control(1)

ALAS-Interp(2)(1) get a position to move to

Web Browser(2) query for movement

Phrases* in <#speak>

352<#play>53<#move>4<#approach>10<#open>2<#refer>3Sumc) “open” tag
MS Agent
Control

ALAS-Interp(1)

Web Browser

424*accurately, Japanese bunsetsu

Table 2: Numbers of tagged representations(1) request for denotationd) “move” or “refer” tag
MS Agent
Control(2)

ALAS-Interp(1) get a position to move to(1)

Web Browser(2) query for movement

Figure 8: Scheme of the ALAS-Interp

Each web page of a TelMeA community involves
an invisible module written in JavaScript. This
module is the interpreter of ALAScript, which iscalled ALAS-Interp. By means of this module, thecommunications between the server and a client ismainly comprised of ALAScript data (Fig. 4). Allcomposed and submitted contributions by ALAScript
are stored in the database, and the appropriate
ALAScripts are retrieved and interpreted in agents’behaviours when the user clicks a title in theconversation list. Figure 8 shows the scheme of
ALAS-Interp.

5 Evaluation of TelMeA System
5.1

Test Use of TelMeA

As a test use, we made TelMeA public in theinstitute, and asked people to use it. Seven people
logged in TelMeA, formed seven communities in it,
and submitted eighteen contributions during theexperiment period (nine days).
Table 2 and 3 show analysis of thesecontributions to show how various types ofrepresentation were used. Here we cut eachcontribution by phrase and classified these cut
phrases by type of representation. Table 3 shows that
contributions involved 17.0% of non-verbal
representations, i.e., those with tags except “speak”
and “think” tag was used in this case. We believe thisfigure indicates that non-verbal representation in
TelMeA was accepted well by users.
Table 3 also shows the detail of non-verbal
representation, i.e., a ratio among physicalrepresentation, use of interpersonal space andcooperative reference. The highest score in the threeis physical representation and the lowest is
cooperative reference. We expected that functions to

Verbal Representation
Physical Representation
Interpersonal Space

83.0%
13.4%
2.4%

Cooperative Reference
Sum ofNon-verbal Representation

1.2%
17.0%

Table 3: Ratio of verbal and nonverbal representations

manage web pages like functions to open and refer
web pages would be used more, since these functions
can make channels of information in communities
wider. We believe longer use may result the different
ratio because we observed many errors when theusers used these functions.

5.2

Result of Questionnaire

After we terminated the test use period, we asked
these users to answer a questionnaire. In thisquestionnaire, they were asked to answer theirsubjective evaluation of the system with five ranks,
where 1 is the most negative and 5 is the mostpositive.
The questionnaire contained 1) four questions
about how the users regard avatar-like agents, 2) ten
questions about users’ evaluation of acceptability
and usefulness of avatar-like agents in a mediatedonline community, 3) eight questions about users’
evaluation of individual representation functions, and
4) seven questions about users’ evaluation of
TelMeA compared to other community systems such
as ML, BBS, and IRC (total twenty-one questions).
Most of mean values of rating for each evaluation
are positive (more than 3), except for the question “I
felt other agents behaviours and facial expressions
were spontaneous,” which is valued equal to 3 onaverage. As long as this evaluation, we think that
TelMeA is a valuable online community system for avariety of users.
In addition, we found further interesting
analytical results from the questionnaire:Recognition of avatar-like agentsSome users recognized avatar-like agents asavatars of their users, while others recognized avatarlike agents as delegates of their users. This
recognition, however, is mostly consistent inindividual users; if a user recognizes the user’s own
avatar-like agent as an avatar then the user
recognizes other avatar-like agents as avatars; thistendency held for recognition as delegates. Only one
user recognized avatar-like agents in an inconsistentway; he recognized his own avatar-like agent as adelegate and the other avatar-like agents as avatars.
In addition, another user recognized her own avatarlike agent in two ways: only when she composed hermessage with her avatar-like agent did she recognize
it as an avatar, but at the other times she recognized
it as an delegate, while the other avatar-like agentswere always delegates to others.
More interestingly, the users’ evaluations of
TelMeA seemed to be independent of the way ofrecognition.
Evaluation of acceptability and usefulness
From detailed analysis for individual users, allusers valued usefulness of avatar-like agents more
highly than acceptability of them. Contrarily, mostusers valued acceptability of a co-presence place foravatar-like agents more highly than the usefulness ofthe place (six users out of seven). As the result shows,functions for non-verbal representation via avatarlike agent and the presence of avatar-like agents arehigh evaluated.
Evaluation of usability
From the results of individual evaluations ofcommunity systems, TelMeA was evaluated highly
as joyful tool rather than a convenient or functional
tool. Users enjoyed their communication with
TelMeA well although its interface is still unnatural
to be improved.

6 Conclusion
In this paper, we proposed an asynchronous
community system named TelMeA, which employs
avatar-like agents. The avatar-like agents embody
presence and enhance awareness in the identification
and relationship of participants. These agents canalso express non-verbal expression. Representationby physical cues, interpersonal space, cooperativereference, and so on leads to conversations in a form
close to face-to-face communications and helps
people to convey messages simply, clearly, andeffectively even in asynchronous conversations.
In future work, we will consider a model
involving avatar-like agents’ conversations to

express recognition of asynchronous conversations
more effectively. At the same time, we will make the
TelMeA system public on the Internet and improve
the interface, especially that of the authoring page.

Acknowledgements
We sincerely thank Dr. Yasuyuki Katagiri and Dr.
Yugo Takeuchi of ATR MIC, who have madegrateful advices for psychological experiments. We
also thank Ms. Keiko Nakao of ATR MIC, who has
designed MS Agent characters we used in the figures.
These characters are designed as a part of a projecton personal agent at ATR MIC.

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