Collaborative Shopping Based on Multi-Agent in Virtual Environments

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



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.

38 7



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

38 8



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

38 9



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

390



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,

l(2002) 281-300.

1997.pp.87-94.

391

Collaborative Shopping Based on Multi-Agent in Virtual Environments

Documents

Transcript of Collaborative Shopping Based on Multi-Agent in Virtual Environments