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8/3/2019 Collaborative Shopping Based on Multi-Agent in Virtual Environments
http://slidepdf.com/reader/full/collaborative-shopping-based-on-multi-agent-in-virtual-environments 1/6
The 8th International Conference on Computer Supported Cooperative Work in Design Proceedings
Collaborative Shopping Based on M ulti-agent in V irtual Environments
2)ZhigengPan, ''Bing Xu, 'IMingming Zhang, 'IHongwei Yang
''State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310027, P.R. China
2, Institute of Virtual Reality and Multimedia, HZIEE, Hangzhou, 310037, P.R. China
(zgpan,xubin,zmm,yanghongwei)@ad.zju. edu.en
Abstract
Existing E-commerce applications on the Internet
provide the users a relativeIy simple, browser-based
interface to access available products. Customers are
not provided with the realistic shopping experience as
they would in an actual store or mall. So this paper
presents a collaborative shopping system based on
multi-agent that can provide the customer s with
simulation of the shopping experience in a real world.
With the creation and application of the avatar in the
3 0 virtual shopping mall, the customers can changetheir role f rom "hollow-man" to human. By the
combination of the sociality with virtual environment,
the system can not only imitates realistic
communication among special customers who are
favorable to the same products but also realize the
communication between multiple avatars.
1. Introduction
With the rapid expansion of the Internet, electronic
commerce (E-commerce) using the web becomes
popular. Nev ertheless, existing E-commerceapplication s on the web prov ide the users a relatively
simple, browser-based interface to access availableproducts. The customers are mainly kept separated and
everyo ne is shoppin g, as if hefshe was in an empty shop.
Thus, customers are not provided with the same
shopping experience as they would in an actual store or
mall [ I ] .
In our opinion collaborative shopping is som ething
people like to do along with relatives and friends. In
particular, it is likely that shopping is a social activity,
meaning that the multiple custome rs can join the
collaborative session to share with each other.
We present the EasyMall system simulating most of
the actual shopping experience through implementing
the collaborative shopping based on creation of the
multi-agent model in a virtual shopping mall
environment. Multi-agent can search and recommendproducts according to the custom er's preference. Using
an avatar chosen from a vast range of avatar identities,
the customers can walk around the virtual environment,
look ov er . and m anipulate the products that he is
interested in and order goods through a security system.
38 6-7803-7941 1/03/$17.00O2003 IEEE.
Our collaborative shopping system creates the same
virtual situation we are used to in real world: multiple
customers can join and do the shopp ing together if they
found that they are looking for the products with the
same products area, they would chat with each other,
ask the others' suggestio ns and find the desired products
more effectively.
The remainder of our paper is organized as follows:
after reviewing the previous work related to ourresearch, we give an overview of the concept in Section
3. In Section 4 describes the fundamental technology in
our collaborative shopping procedure. Implementationof collaborative shopping in EasyMall is presented in
Section 5. Finally, we summarize the contributions withfiture research in Section 6 .
2. Related Work
There has been a tremendous amount of previouswork on creating a collaborative work and interactionspaces across networks, with notable contributions from
the fields of Com puter Supported Collaborative Work,Groupware, and Computer Human Interaction [Z j . In
this section we briefly mention the previous work that
come closed to our work.
Xiaojun Shen [l ] presents vCOM, a VRML and
Java3D-based prototype, which permits users tonavigate around virtual e-commerce worlds and p erform
collaborative shopping and intelligent searches with the
assistance of software agents. Real-time interactions
between the entities in this shared enviro nme nt are
implemented over the H igh Level A rchitecture (HLA),
an IEEE standard for distributed simulations and
modeling. Chia-Hui Wang [3] introduced a Web-based
information system for providing remote surveillance
services. The surveillance data can be treated as a kind
of digitized product in E-market, which collaborative
different value-added services with the same
surveiIlance data. S. Puglia [Z] proposed a component-
based architecture for collaboration that provides shared
navigation of the WWW along with an EJB-based
server implementation. As a particular application builton this architecture, they present MuItECommerce,
through which multiple users can participate in virtual
shopping trips among multiple shopping sites.
Promondia [4] provides a client-server architecture,based on the distributed of appfets, to support
8/3/2019 Collaborative Shopping Based on Multi-Agent in Virtual Environments
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collaborative tasks such as text-based chat, shared
whiteboards, voting and surveys and implements asystem where the interaction is limited to one
Protnondia server distributing page and services. Our
EasyMall system is a multi-server based on agents [ 5 ] ,
which uses blaxxun to implement the interaction
between customers and the virtual environment. Its goal
is to create an interactive virtual mall with integrated E-
commerce, agent technology and virtual reality. VRMLand Java3d technology is employed to provide avatar
for each customer who can communicate with otheravatars as well as products in mall and agents [ 6 ] .
3. System Overview
We brought forward the EasyMall system based on
the client-server architecture to develop and implementa collaborative shopping session. Basically, thecollaborative shopping system can be divided into four
layers as shown in Figure 1.
IJava3DNRML-based browser
Figure 1.An overviewof the EasyMall systemarchitecture
Client Layer: as for the distribution of the shopping,
eveiy user in the client layer may collect items from the
different m erchant sites he is interested in. Th e locationthe user is visiting, expressed in terms of the UlUs of
the 3D virtual environment he currently navigates fromhis terminal.
Interaction Layer: at client site, the interaction layer
provides the interaction interface for every user. The
browser based on Java3D or VRML mode offers some
performance advantages to users, such as choosing the
avatar, chatting between avatars and controlling theroam o f avatar, etc.
Agent Engine Layer: the agent engine layer plays a
pivotal role, which supports the communications of the
collaborative shopping in virtual environments. Theagent engine layer can be seen as including two
consecutive high-level processes: multi-agent and
collaborative management. In the multi-agent level,there are three agents: Recommendation agent, database
agent and manipulation agent. A detailed description
will be showed in Section 4. The collaborative
management level is also included in the layer, which is
composed of object ownership administrator, security
and sce ne control. In Section 4, we will discuss these in
deta i 1.Server Layer: the server layer includes multiple
merchant servers. Every merchant server stores the
various products and merchants information. The agent
engine layer communicates with the server layer in xmland accesses the relative d ata.
4. Collaborative Shopping based on Multi-
agent
In this section, we introduce the core module of the
system. We start with explaining the main componentsof the multi-agent. Then, we describe the collaborativemanagement an d its components.
4.1. Multi-agent Mechanism
To offer accurate and required products which thecustomer wants to purchase, the EasyMall system used
multi-agent which can provide the customer withselection and manipulation of the products according tocustomers' preference. During the procedure, databaseagent is also used to manage and manipulate varieties ofdata such as products database, sales database andcustomer. profile data. Th e framework of multi-agent isshowed in Figure 2 .
I I ,III
II
"€ern IIIIII
Shared+
!
Figure 2. Multi-agent structure
In the following section we describe how the multi-agent guid es customers to purchase.
(1) Database agent
In current E-commerce, the most popular data modelfor a data warehouse is a multidimensional model. Sucha model can exist in the form of a star schema, a
snowflake schema, or a fact constellation schema.
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Figure 3 shows the star schema which is used in
EasyMall system. Purchasing is considered along four
dimensions, namely, products, time, customer, andmerchant. The schema con tains a central fact table for
purchase that contains keys to each of the four
dimensions. To minimize the size of the fact table,
dimension identifiers (such as time-key and
merchant-id) are system-generated identifiers [81.
Dimension tables Fad able Dimenson tablesCustomer table Purchase table
uataner k g r r m ecustaner ap
ulStaner gendercustanerMty
med aot tab le
merchant typemerchant raig
--I--
time table
produd table I
Figure 3. Star schema of a data warehouse for
purchase
Notic e that in the star schema , each dimension is
represented by only one table, and each table contains aset of attributes. For example, the customer dimension
table contains the attribute set (id, name, age, gender,
hobby}. In order to divide customers into different
groups and recommend products to target group, we
design Database agent based on the star schema so that
it can satisfj, the need to understand the behavior of
business units such as custom ers and products.Database agent stores all information con cerning the
user profile, the purchase history records, virtual
environment including characteristic of avatars, the
history of their en counte rs with othe r avatars and the
click of user, so that the users can not only berecommended the favorable products but also be
presente d with information about the current situation of
and en counte rs made by the avata r in the virtual mall
when the user logs in and chooses his her avatar.
(2) Product selection agent
When the customer enters the EasyMall, he first
inputs his wishes or demands. But if the system asks the
customer to input a lot of require information in which
customers are not interested, it will not only guide the
system to find many irrelevant products but also anno y
the customers and finally lead them to log out the
system. To avoid this situation, Product selection
requires that products be described in terms of a set of
measurable features that the customers are concerned
about. The selection and quality of the features
representing each customer’s requirement have a
considerable bearing on the success of subsequent
selection.
Although a domain expert can design the textdialogs, the best methods to show his individual
preferences can be expressed by the custom er. Decision
tree induction can be used for attribute subset selection.
A tree is constructed from the given d ata. All attributes
that do not appear in the tree are assumed to be
irrelevant and can be deleted. Rough set can also
implement this goal. Here, we present an approach to
feature extraction in which feature selection, featureextraction, and classifier training are performed
simultaneously by the integration genetic algorithm and
case base reasoning. Figure 4 shows the flow chart of
the algorithm.
Feature vector Feature extractioneature mas ure m
Figure 4. The flow chart of the algorithm
0 The first step: Throug h the questionnaire, we
0
0
0
obtain the feature vector.
The second step: using GA to select the features.
The third step: generating new feature vector and
each feature has different weight.
The fourth step: we use CBR as the classifier. The
CBR classificatio n has been se lected for use incombination with the GA feature extractor for two
reasons. The simplicity of the CBR classifier
makes it easy to im plem ent. Weig hts used incomputation of similarity can represent each
feature’s importance, which can improve
prediction accuracy.In the following, we describe the comb ination of GA
and CBR technology in detail.
Genetic algorithm
Algorithm GA: G enetic Algorithm
Step 1: Initialize a population of chromosomes
Step 2: evaluate each chromosome in the population
Step 3: create new chromosom es by mating current
chromosom es; apply mutation and recombination as the
parent chromosom es mate.
Step 4: delete members of the population to make
room for new chromosomes.
Step 5 : evaluate the new chromosomes and insert
them into the population.Step 6 : f the stopping criterion is satisfied, then stop
and return the best chrom osome; otherwise, go to step 3 .
Chronxmrne i
000000000 I 101111010 I 000000000 I ... I 110001101
N*(+8 ) bits
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N-dimensional vector comprises a single member ofth e GA population for G A-based feature selection. Eachvector has 9 bits. The GA feature extraction technique
has been expanded to include a binary masking vector
along with the feature weight vector o n the chromosome.If the mask value for a given feature is zero, the feature
is not considered for classification such as feature 1. Ifthe mask value is one, the feature is included in the
classifier such as feature 2 and its weightw 2
s01111010
Since the particular GA engine used maximizes the
objective function, the following object function is usedto evaluate each chromosome:
Object function = No. of masked features + No . of
correct predictionsDuring the process, presuming that only those
chromosomes that can classify the training set withhigher accuracy than the given threshold can join into
the next population and those chromosomes below the
given training accuracy can be deleted.
Case Base Reasoning
‘The intelligent assistant uses the GA technology tocapture the customer’s demands and wishes. Then basedon this information, the intelligent assistant converts theinaccurate information into fuzzy variables in terms ofits membership functions and then uses Case Base
Reasoning (CBR) methods to choose the appropriate
products. CBR classifiers are instanced-based. Whengiven a new case to classify, a case-based reasoning willsearch for training cases having components that are
similar to those of the new case. The key technology inCBR is finding similarity measure. Here, a query q is
described by the attributes q , , q I and a case c is
described by c 1 , 2 ... C , , the similarity 0 can be
defined as
(4 > c )= 2, = I
WicTi
‘Where the weights w, and q , can be acquired by
GA.
(3) ]Product manipulation agent
As to the manipulation agent, it allows customer to
conlrol an avatar to do some motions so as to inspect the
products he customized through the animation playing
with a third person view point or operate products withthe first person viewpoint. Product manipulation agent
is also used to control the behavior of objects. W hen theavatar picks up the products, he can look over the
products from different angles and can also read therelevant information about the products through text,image and video.
For example, when an avatar enters the clothinglocation, she can select the favorable product and try it
on. If she is not satisfied with the color or texture of the
skirt, the product manipulation agent can help her to
change the texture based on the texture morphing
method [9] (depicted on Figure 5).
Figure 5. The simulating of skirt
4.2. Collaborative Management
The collaborative management is the central level ofthe collaborative shopping system. The current virtual
market places often lack in the emulation of the socialinteraction factors [7]. So in our system we combine thesociality with virtual environment. On the first log in,the user chooses hisher avatar representative by
registering information; therefore, the customers changetheir role from “hollow-man” to human. The customersmight “walk” and select products in the virtualenvironment by avatar, they can also invite other avatarsto meetings with one’s own avatar or with accepting
invitations for the own avatar.The collaborative management allows the customers
to manipulate the products in natural manner such asone-customer selects T-shirt from the costume area.
In the following section, we will introduce the
components of collaborative management.
4.2.1. Object Ownership Administration
The ownership of object is one o f the pivotal models
in the procedure of collaborative shopping. The
collaborative management module provides theownership core based on mutex lock mechanism for
exchanging attribute ownership among multiple avatars
in the shared virtual space so as to ensure that only one
avatar at a time has access to the pro duct.
Pthread-a mutex; //definition of the mutex
semaphorePthread-mutex-init(mutex, mutexattr); // initialize
the mutex variable, the first parameter is the variable
that will be initialized, the second parameter is the
attribute of the mutex variable,Pthread-mutex-lock(mutex); // lock the mutex
variablePthread-mutex-unlock(mutex); // unlock the mutex
variable
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The ownership core uses the above sub-function to
administrate the object ownership, which permits onlyone avatar to handle the object, so multiple avatars
exclusively manipulates the attributes of an owned
object (such as orientation and location). If another
avatar attempts to operate the o bject whose ownership
has been d istributed , the ava tar is inserted in the waiting
queue until the ownership of object was released. For
instance, when an avatar clicks the T-shirt that he wantsto operate, he/she first needs to request the ownership ofobject by using the ownership core. If the ownership of
the T-shirt has already been d istributed , the avata r has
to wait until the ownership is given back. Then,
ownership core allots the ownership to other avatars
who are in the waiting queue according to the priority
and waiting time. After that he/she can pu t the T-shirt
back by releasing the o wnership to its original owner.
4.2.2. Security
The security, another pivotal model, is responsible forcontrolling the privacy of custom ers and merchants.
In fact, the collaborative shopping in virtual
environment is done through the interaction of remoteobject between the client layer and server layer. In the
system, we use Java and Java3D to implement theobject remote control. The problem we encountered is
due to restrictions imposed by the Java security model
on remote access to an applet. Firstly, the absence ofsupport for multiple inheritances in Java made it
impossible to have an applet, which already inheritsfrom the Java Applet class, also extend the Unicast
Remote Object (which is n ecessary to make an objectremote). Secondly, there seem to be server securityobstacles preventing the server layer from opening aseparate socket to actively “write” on a remote object
bound to the ap plet. The problem is that an applet maynot open a server socket to listen for requests from the
arbitrary hosts [ 2 ] .
As to privacy, we used the security model to provide
the mechanism to degrade or enhance the custom ers and
merchants’ privacy. On the one hand, the security modelenables the possibility to provide anonymity to
customers since it could prevent cookies and other
private data from being transmitted to the merchants,
acting as a filter between the two. On the other hand, the
information that is available to the security model
relating to custom er identity, preferences and shopping
habits would be of great value to m erchants and vendors.
4.2.3. Scene Contro l
The traditional distributed system is based on acentral server model. In this model clients communicate
with a central server, which m anages the entire system
and informs clients of any updates and changes in thestate of avatars and objects. Clients only communicate
with the stand-alone server, which contains the entirescene database and tracks all objects of interest within
the system [lo ]. Espec ially, in large virtual wo rlds the
number of objects that require certain synchronization
or update messages to be transferred over a netw ork can
slow down the interaction of the ind ividual user with the
shared world in an unreasonable way [ 2 ] .A solution for
this problem is the subdivision of large virtual worlds
into several regions or zones [l 11.
For the collaborative shopping environment, we
partition the virtual environment into several separateareas. Each o f the areas in the virtual environmen t hasits own boundary and world coordination. In our
approach, we use the Proximity Sensor of VRML to
listen the avatar’s coordination. The coordination is
represented by [e,X , Y ,21, whereQ represents the ID
of certain shopping area which the avatar is in,
( X ,Y ,Z)represents the avatar’s position information in
the shopp ing area. For example, when an avatar walking
inside the boundary o f T-shirt area, the sensor transmit
the information to scene control model, so it refreshes
and synchronously updates the corresponding data onthe participating avatar.
5. Implementation of CollaborativeShopping
We implemented a multi-user collaborative shoppingvirtual environment by using VRM L, Java3d, artificial
intelligent and computer network technology. In oursystem, we provide the user with simple and efficient
navigation tools to visit the multiple virtual malls
(depicted in Figure 6). The EasyMall system providesthe multi-agent model to allow the avatar comm unicatewith the intelligent guider. When customers enter thevirtual mall, the intelligent guider help customers findout what they really want. When the customers can
simply identify the type of product they need anddescribe the features or specific functions of theproducts.
Figure 6. Multiple virtual malls (Entrance, ProductsSelection Area, Garment Area and Furniture Area)
Customers might interact with other avatars in the
shopping procedure. If two avatars meet in a T-shirt
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more effectively he lp the customers find and cho ose thedesired goods [2].
Figure 7 shows an example in th e EasyMA systemwhere two avatars collaboratively buy T-shirt. Thefemale avatar finds a beautiful T-shirt, show s it to themale avatar and asks his opinion.
Figure7.Collaborativeshopping in avirtualT-shirt store
6. Conclusion and Future Work
We have presented Easymall, a system aimed tosupport collaborative shopping based on multi-agent invirtual environment. With the creation and applicationof the 3D virtual shopping mall, the customers mightsimulate the shopping experience in a real world byavatar. We have integrated virtua l reality a nd intelligenttechnology into E-commerce to generate a collaborativeshopping prototype. The prototype not only imitatesreapistic communication among special customers whoare favorable to the same products, but also provideshuman-like in teractive interface.
Our future work will design a virtual memory w ith
the smart object technology to implement multipleavatars’ autonomic behavior in virtual environment,
which can improve the performance of collaborative
shopping.
7. Acknowledgement
This project is co-supported by Excellent YoungTeacher Award Project of MoE, 973 Project (grant
no: 2002CB312100) an d European E LVIS Project.
8. References
[11 Xiaojun Shen, T.Radakrishnan, Nicolas D.Georganas.
“VCOM : Electronic commerce in a collaborative virtualworld”, Electronic Commerce Research and Applications
[2] S . Puglia, R. Carter, and R. Jain, “MultECommerce: A
distributed architecture for collaborative shopping on the
www,” in ACM Conference on Electronic commerce,2000.19.
[3] Chia-Hui Wang, Ray-I Chang, Jan-Ming Ho, “A nEffective Communication Model for CollaborativeCommerce of Web-Based Surveillance Services,” IEEEInternational Conferenceon E-Commerce, 2003.
[4] Gall U., Hauck F.J., Promondia: A Java-Based Frameworkfor Real-time Group Communication in the Web,Proceedings of the Sixth International World Wide WebConference (1997).
[5] Zhigeng Pan, Bing Xu , Tian Chen, Fangshen Wu. “Designand Implementation of an Interactive 3D Virtual ShoppingSystem EasyMall”. Journal of CAD&CG (To appear).
[6 ] Qingge Ji, Bingrong Hong, Dongmu Wang. “MobileAgent based Prototype of Heterogeneous DistributedVirtual Environment Systems”, Journal of SystemsEngineering and Electronics, 2000, l l(2 ): 61-65.
[7] T. Schuemmer, GAMA-Mall-Shopping in Communities,in: Second International Workshop on ElectronicCommerce (WELCOM’Ol),Heidelberg, 2001.
[8] Jiawei Han, Micheline Kambr “Data M ining Concepts andTechniques” Morgan Kaufmann Publishers.
[9] P.Wang, M.M.Zhang, Z.G.Pan, New texture morphing
method for visual presentation of tex tile product, SecondInternational Conf. on Image and Graphics, Aug.2002,HeFei,China,PP1011-1016.
[IO] P. Morillo, M. Fernandez, N. Pelechano: A GridRepresentation for Distributed Virtual Environments.2003 Annual Crossgrid Project Workshop & 1st EuropeanAcross Grids Conference, February, 13th-I4 th, 2003.
[ll] W.Brol1, Populating the Internet: Supporting MultipleUsers and Shared Applications with VRML, in:Proceeding of the VRML‘97 Symposiun, (Feb. 24-28,1997, Monterey, CA), ACM SIGGFLG”, ACM,
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