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8/3/2019 Agent-Based Interaction Model for Collaborative Virtual Environments
http://slidepdf.com/reader/full/agent-based-interaction-model-for-collaborative-virtual-environments 1/4
Th e 9th lntemational Conference on Computer Supported Cooperative Work in Design Proceedings
Agent-based Interaction M odel for Collaborative Virtual Environments
Xiaohong Mi, Jiaxin ChenElectron. In$ Eng. Coll. Henan Univ.of Sci. & Techno,He 'Nan Province, China
cjx@mail.haust.edu.cn
Abstract
Interaction among users in the context of
CoIIaborative firtual Environments (CVEr) afects the
eflciency of collaborative work In mosi of the current
CSCW application systems, users interaction is stillbused on the traditional ways such as @ped chat and so
on . In order to enrich the interaction among users,
sumeone has propo sed to add 3D avatars into the CVEs.
Howevel; the poo r behavior usually shown by the avatarscontrolled by users makes it dtficult to achieve un
acceptable level of immersion or their users. This paper
provides with a new point of view, prop osing an
agent-based model fo r the study of avatar'interaction in
CVEs through the analysis of dryerent interaction laversamong users, and presenting a semi-autonomous avataruppronch. By attaching Q semi-autonomous intelligent
virtual agent to the avatars, we can enhance theimmersion and interaction among users.
Keywords: Collaborative Virtual Environments;Interaction; 3D Avatars; Intelligent Agent; DecisionMechanism
1 Introduction
The concept of Computer Supported CooperativeWork (CSCW) has broken through the traditionalapplication of computer, for it provides users with aWYSIWIS (What You See Is What I See) C ollaborativeVirtual Environments [l]. In addition, CVEs also allow
users to collaborate in closely coupled and highlysynchronized tasks, These tasks require very closecoordination between two or more users. But In most ofthe current CSCW application systems, users' interactionis still based on the traditional ways such as typed chat
and so on. Thus, it is necessary to provide a means ofinteraction amon g users for better collaboration. In order
to enrich the interaction among users, someone hasproposed to add 3D avatars into the CVEs. Havingavatars as user representations in CVEs stems from the
need of an identity that every user feels when he enters
into the environment. It has several other functions:inform the user's presence to others, identify anddifferentiate users, visualize the users position and
orientation, direction of interest, and enable
communication among users [2]. However, the poorbehavior usually shown by the avatars controlEed by
users makes it difficult to achieve an acceptable level ofimmersion for their users.
The solution that we propose is to automate thestatic avatars, trying to make them interact in the same
way as what users would do in real world. We advocatefor the attachment of a semi-autonomous intelligent
virtual agent to the avatars. By using AI techniques, wecan build intelligent agents, and then the user can choose
to take absolute control over the actions of his avatar,delegating the man agement of the rest to the agent. With
more intelligence, avatars are able to perceive theenvironment and to make their own decisions, and thus
can enhance the interaction among users.
Some previous works have also dealt in some way
with the partial autonomy of avatars in an interactiveenvironment. One of th e most interesting proposals is
The CyberCafe, described by Rousseau and Hayes-Rothin [3]. They introduce the concept of synthetic actors. Asynthetic actor may be autonom ous or a user's avatar. An
autonomous actor receives directions from the scenario
and other actors, and decides on its own behavior on the
virtual stage with respect to those directions [4]. Anavatar is largely directed by a user who selects actions toperform, although it also receives directions from th escenario and from the other actors. In fact, the user
chooses the actions to be perform ed by the avatar, but theway to cany them out is chosen by the avatar. Theseactors are able to improvise their behavior in aninteractive environment and they own a repertoire of
actions that are automatically planned to achieve each
goal.The first problem of automating part of the behavior
of an avatar is that if the user decides to delegate some
functions on her personal agent, she will expect the
behavior exhibited by the avatar to be similar to he r own
behavior in the same situation. She will also expect her
avatar to behave in a consistent way. And, moreover, shewit1 expect a different behavior of her avatar towards thedifferent avatars that populate the virtual world. In order
to do so, the intelligent agent attached to our avatars
must be able to manage several knowledge dimensions,such as user dimension and so on. And it also needs a
decision mechanism that allows it to select the mostappropriate action in very situation,
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The9th International Conference on Computer Supported Cooperative Work in Design Proceedings
This paper goes into a description of the different
interaction layers among users, and shows the limitationsof the current CVEs in each of these layers. Then we
describe the set of dimensions in the virtual agent’s
knowledge base that are needed to enhance users’
interaction. Afterwards, the application of theagent-based interaction model in a CV E is discussed and
some xperimental results are presented.
2 Interaction Layers
The design of CVEs enhances the collaboration ofusers, and the 3D avatar is an important factor. In amulti-user collaborative environment, if someone wants
to know others’ present work, he has to achieve it by
observing the actions of their avatars [ 5 ] . Considering the
actors of CVEs are not only the users but also theiravatars, we propose a four-interaction-layer as follows:user-user interaction, user-own avatar interaction,
user-other’s avatar interaction and avatar-avatarinteraction. It’s shown as Figure 1.
should be improved by adding more intelligent
capabilities to the avatars, thus increasing the user
immersion as well.
2.3. User-another’s avatar interaction
Entering most CVEs we can only find inexpressive
and static avatars, because they are merely used as asignal to indicate the presenc e and location of their users.
Once they have met, the communication turns to thetraditional user-user Iayer. If the avatar can provide some a
information about its owner, such as name, e-mail
address, vocation and so on, it can lead into a
reinforcement o f the interaction among users. This can
be accom pIished by building a user knowledge database.
2.4. Avatar-avatar interaction
In current CVEs, since avatars are not aware of
anything, they cannot interact intelligently with otheravatars without the intervention of their users. With more
intelligent avatars, which are able to perceive the
environment and to make their own decisions, this
interaction layer could be exploited to enhance user-user
interaction and to make avatars more useful for their
owners.
3 Architecture of an IntelligentAgent
Figure 1. Interaction layers among users
2.1. User-user interaction
This i s the kind of interaction in which users
communicate directly without the invention of their
avatars. Typed and voice chat is the most common tool
for this kind of interaction. People can discuss some
collaborating problems an d acquire others’ present work.However, it is neither natural nor fast.
2.2.User-own avatar interaction
The communication between a user and his ownavatar is one of the poorest exploited. Most of the currentCVEs consider the avatar just as a puppet that receives
commands and executes them without doing anyintelligent processing or learning, and they have no
awareness of the others [ 6 ] . The direction in layer 2
We should analyze, as a starting point, some of themost remarkable ideas of previous works. A goodapproximation to the architecture of an avatar is The
CyberCafe[3]. According to this architecture, a
participant has a mind and a body. We have adopted thearchitecture that is shown in Figure 2. Each avatar is
implemented as i t ~ l gent, i.e. something: ‘?hat can be
viewed as perceiving its environment through sensorsand acting upon that environment through effectors” 171,
which consists o f tw o main components: a physical bodyand an AI engine. The body is the 3D geometric
representation of the watar (together with its positionand speed), which provides the AI engine with allnecessary sensing and actuator services, whereas the AI
engine (mind) supplies all functionality necessary forworld representation, goal planning, sensing and acting,and emotions. This feature will allow us o cope with theunexplored interaction layers.
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Th e 9th nternational Conference on Computer Supported Coop erative Work in Design Proceedings
Figure 2. Architectureof an intelligent avatar
Within this architecture, ou r aim will be the
description o f the avatar’s mind. The mind will control
the actions to be performed by the avatar’s body in the
virtual world. .In order to build this mind we havedeveloped an intelligent agent that can be linked to the.
avatar.
In fact, an intelligent agent is a computer systemcapable of flexible autonomous action in some
environment. The main features of agent is showed as
follows:(1) Autonomy: Capable of acting independently,
exhibiting conbol over their internal state by flexible;(2) Social Ability: The ability to interact with other
agents (and possibly humans) via some kind ofagent-communication language, and perhaps cooperatewith others, and agents interact with environment
through sensors and effectors as shown in Figure 3;
Figure 3. Principle of agent
In order to perform the most appropriate actions invery situation, the agent must provide a set of decision
mechanism that depends on the following howledge
base [XI:
(1) User Dimension: First, th e agent must have
knowledge about its own user, in order to behave in thesame way she would do . It has to learn about her goals,
her concentration , her reactions, her personality, her likesand dislikes, etc.
(2 ) Introspective Dimension: O n the other hand, the
agent must manage some knowledge about itself (itsmind) and the avatar it is controlling (its body): external
appearance, personality, moods, past experiences,
location in the CVEs, etc.(3) Social Dimension: A third kind of knowledge to
be managed is to concern the rest of the avatars
inhabiting in the CVEs: their appearance, personalitytraits, mood, attitud es, past history of interaction, etc.
(4) Environment Dimension: Finally, the agent alsohas to manage some know ledge about the CVEs in which
itis
located: geometry, objects, exits, utility, etc.Among the interaction model we have advocated,sensors apperceive the information of environment with
it s own environment dimension, and then it will decide
on what to do and how to do it according to this
information and it s knowledge base; afterward, effectors
will perform the corresponding actions. Of course, this
needs an action database, too. Thus this model can
improve the interaction Iayers by delegating somefunctions on their personal agents. The structure of th e
intelligent agent we advocate is shown in Figure 4.
4 Algorithm for sensors an d effectors
The key elements of agent are it s sensors and
effectors. Every time an avatar performs an action, theagent attached to it first senses the environment via avision cone, if it gains awareness of other avatars in its
path, it will analyze this information, and then send it tothe decision mechanism; in the end, the effectors ‘select
Sensors 5 --l
Environrnent Dimension c
r
Decision Mechanism What f should do n a w
1
Agenti
effectors -Figure 4. Structureof intelligent agent
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The 9th nternational Conference on Computer Supported Cooperative Work in Design Proceedings
the proper actions for the avatar to perform. The logiccontrol of the avatar’s behavior is as shown as follows
too many controls. It ha s enhanced the interaction amongusers in some dew ee. However. the model we have built
image = Body.Sense0
return VisionCone.GetImage0I
1
IMind.UpdateWorldMode1 (image)
Knowledgeaase. Modifyworld (image)
WorldModel.ModifyWorld (image){.
11
IMind.ReviaePlan 0
ActionPlanner.Plan 0
KnowledgeBase.GetGoal8 0
ExploreSolutions 0Knowledge8ase.GetObjectInfo 0
WorldModel.GetObjectAttribs ( 1
Createplan ( 1
lastArtion = SelectLastPlannednctionOMotionContro1.Decompose (1astAction)
{
1
11
(action = Mind.PickAction0
microA = ActionPlanner.GetMicroAction0
return MotionControl.GetCurrentAction()
return microA
(
1
I
Ireturn ConvertActionToEvent (act on)
//Acting algorithmDoActing (detailedType1
switch deta ile dwe
SetHeading ( 1Setvelocity ( 1Setposition ( 1
I
case STEP
{
)
{SetHandPosition ( )
1case NOD
(
1
caQe MOVEHAND
. . .
Actuator.ExecuteChange 0
AnimationScriptF1LE.write (line)
return ne w SensingEvent
t
1
1
5 Conclusions and Future Directions
now is very simple, much work should be done in later.
The hr ther work will concentrate on the implement ofthe knowledge base and action database to provide more
flexible interactions between avatars, thus the user candelegate more action managements to the agent.
Renference
Grudin, “Computer Supported Cooperative Work:
History and Focus”, IEEE Computer, May 1994, pp .
B.Roehle, “Channeling the data flood’, IEEE pectrum,
Rousseau, D. an d Hayes-Roth, B., Improvisational
Synthetic Actors with Flexib le Personalities,Report No .
KSL 97-10, Knowledge Systems Laboratory,
Department of Computer Science, Stanford University,
Stanford, Califomia, 1997.Hayes-Roth, E., Brownston, L., Sincoff, E, Directed
Improvisation by Computer Characters. Technical
Report KSL-95-04. Knowtedge Systems Laboratory..
Stanford University. Stan ford. California, 1995.
Wenfeng Guo, Yingying Wang, Achievement of .the
dynamic control over ava tar actions in VRML worlds”,
Microcomputer and its Application,2002(10), 55-57 .
Herrero. P., Amusement Project Deliverable 5.ld-
Awareness of Interaction and of Other Participants.
Amusement Esprit Project 25197, 1998.
Russell S., Norving P., Artificial Intelligence,A Modern
Approach, Prentice hall, 1995.
h b e r t , R., de Antonio, A., Sanchez-Segura, M. I.,
Segovia, J., ‘Wow Can Virtual Agents ImproveCommunication in Virtual Environments?”, In
Proceedings of the Second Workshop on Intelligent
Virtual Agents, VA’99, Salford, UK, 1999,pp. 139-142.
19-26.
March 1997, pp. 32-38.
.
[9] Adma Szarowicz, Juan Amiguet-Vercher, Peter Forte,
“Multi-agent Interaction for Crowd Scene Simulation”,
American Association for Artificial Intelligence,2001.
In this paper, we have discussed about the
attachment of intelligent agents to avatars, and advo catedit as a way to soIve the shortage o f interaction layers in
current CVEs. An agent-based interaction mode1 has
been advanced. By using AI techniques, we have builtsome simple intelligent agents. With more intelligence,
avatars ar e able to perceive the environment and to maketheir own decisions without overloading the user with
404
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