THAM 154

A NEW ERA OF MOBILE SHOPPING BASED ON INTELLIGENT FUZZY NEURO-BASED SHOPPING AGENTS

Dr Raymond S T Lee

Department of Computing T h e Hong Kong Polytechnic University HungHom Kowloon Hong Kong

Email csstleecomppolyueduhk

Abstract- Owing to the increasing number of mobile e-commerce upplications using WAP tecknology intelligent agent-based systems has become a new trend of development in the new millennium Traditional web-based agent systems suffer various degrees of deficiency in terms of the provision of intelligent software interfaces and light-weighted coding to be implemented in WAP devices In this paper the author proposes a comprehensive and intelligent-based agent platform known as intelligent Java Agent Development Environment for the development of smart (via the implementation of onscious Layer y compact and liiglily mobile agent applications From the implementation point of view the author introduces an intelligent fuzzy neural-based mobile shopping agent based on the integrution of WAP and Java Servlets technology Promising results in terms of agent mobility fuzzy-neural shopping efficiency and effectiveness have been obtained

INTRODUCTION The exponential growth of the e-commerce in recent years

brings new chances for business However contemporary agent systems such as IBM Aglets [ I ] and Objectspace Voyager [2] focus on the mobility and multi-agent communications The core functions of intelligent agents (IA) - the AI (Artificial Intelligent) counterpart are difficult to implement In a typical e-shopping scenario most of the time we are handling inexact product selection criteria Current shopping agents such as Bargain Finder [3] and Jango [4] provide agent-based price comparison facility however comprehensive agent brokers negotiation schemes are failed to apply Moreover all of the above e-shopping systems are designed for fixed terminals With the advance in consumer electronics and mobile computing technologies handheld computing devices such as palm computers and WAP phones are becoming increasingly economical and popular This opens new opportunities for developing more user-fi-iendly mobile shopping services For example a consumer can dispatch an intelligent shopping agent to the network anywhere and control the agent activities through a WAP device

INTELLIGENT FUZZY NEURO-BASED SHOPPING AGENTS In this paper the author proposes a comprehensive and truly

intelligent agent-based framework known as intelligent Java Agent Development Environment To compensate for the deficiency of contemporary agent software platforms the model provides an ingenious layer called the Conscious (Intelligent) Layer which implements various AI functionalities into multi- agent applications With the integration of Java Servlet technology in the Technology Layer of the model the proposed mobile shopping solution provides an innovative intelligent agent-based solution in Mobile Electronic Business (MEB) with the integration of four different technologies 1) WAP technology for mobile e-commerce (in the Support Layer) 2) mobile agent technology based on Aglets (in the Technology Layer) 3) Java servlets for servlet-side agent dispatch in WAP servers and 4) AI capability in the Conscious Layer using fuzzy-neural networks

as the AI backbone - an extension of the previous research on hzzy agent-based shopping using fuzzy shopping technology [5] Figure 1 shows the system architecture of the proposed model 161

I

Figure 1 System architecture of the intelligent Java Agent Development Environment (version 1 O)

IMPLEMENTATION From the implementation point of view the shopping solution provides an integrated intelligent agent-based solution for Internet shopping (e-shopping) as well as mobile shopping (m- shopping) via a WAP device Figure 2 depicts the system framework of hzzy agent shopping fiom WEB to MEB-based shopping which consists of the following six main modules Customer requirement definition (CRD) Requirement fuzzification scheme (RFS) Fuzzy agents negotiation scheme (FANS) Fuzzy product selection scheme (FPSS) Product defuzzification scheme (PDS) and product evaluation scheme (PES) For system evaluation a product database of over 200 items under eight categories were used to construct the e- catalog These categories were T-shirt shirt shoes trousers skirt sweater tablecloth napkins The author deliberately chose softgood items instead of hardgoods such as books or music (as commonly found in most e-shopping agent systems) so that it would allow more room for fuzzy user requirement definition and product selection Two tests have been conducted 1) Round Trip Time test and 2) Product Selection Test Round Trip Time (RTT) Test

Two Agent Servers were used in the test Tlserver and T2server Tlserver was situated within the same LAN of the client machine while T2server in a remote site Results of the mean RTT after 100 trials for are shown in Table I In summary total RTT is dominated by the FPSS however the total time taken is still acceptable 5 to 7 seconds Besides the difference of RTT between the server situated in the same LAN and the remote site was not significant except in the FANS whereas Fuzzy Buyer needs to take a slightly longer trip than the other

270 0-7803-6622-001 $1000 0 2001 IEEE

Figure 2 Intelligent shopping scenarios for I ) Internet Shopping (e-shopping) 2) Mobile WAP-based Shopping (m-shoppmg)

TABLE I

A In WAP phone amp WAP gateway (m-shopping)Client browser (e-shopping) CRD I RFS I 25 I 73 I 310 1 305

B In Cyberstore (both m-shopping and e-shopping) FANS I 225 I 1304 I 320 1 2015 FPSS I 3120 1 3311 I 4260 I 4133

C In WAP phone amp WAP gateway (m-shopping)Client browser (e-shopping) PDS 1 310 I 335 I 320 I 330 PES I 53 I 102 I 251 I 223

Total RTT I 3733 I 5125 I 5461 I 7006

Product Selection (PS) Test Unlike the RTT test in which objective figures can be easily

obtained the PS test results rely heavily on user preference In order to get a more objective result a sample group of 40 candidates was invited for system evaluation In the test each candidate would buy one product from each category according to hidher own requirements For evaluation they would browse around the e-catalog to choose a list of the best five choices (L) which fit hidher taste In comparison with the top five recommended product items (i) given by the hzzy shopper the Fitness Value (FV) is calculated as follows In the calculation scores of 5 to 1 were given to correct matches of the candidates first to fifth best five choices with the fuzzy shoppers suggestion For example if out of the five best choices selected by the customer products of rank no I 2 3 and 5 appear in the fuzzy shopper recommended list the fitness value will be 73 which is the sum of I 2 3 and 5 divided by 15

Four different product selection schemes were adopted 1) Simple product selection (using product description matching - traditional technique) 2) Product selection based on FFBP neural network training 3)Product selection based on fuzzy product

description - no network training is involved 4) m-shopper - product selection based on fuzzy-neural training

The corresponding Fitness Values (FV) and the degree of improvement (against the traditional technique) under the eight different product categories are shown in Table 11 Table I I - Fitness value for the eight different product categories

under different product selection schemes

I Fitness Value FVK IK Imorovement)

CONCLUSION In this paper the author proposes an innovative mobile shopping solution based on fuzzy-neural based intelligent agents hopefully it will open a new era of intelligent-based mobile e- Business in the new millennium References [ I ] [2] Voyager URL httpwwobjectspacecomvoyager [3] [4] Jango URL httnwianrocom [5]

Aglets URL httpwwwtrlibm CO jpiagletsl

Bargain Finder URL httobf cstar accombf

R S T Lee and J N K Liu Fuzzy Shopper - A fuzzy network based shopping agent in E-commerce environment In Proc of MAMA2000 December 1 1 -13 Wollongong Australia 2000

[6] R S T Lee and J N K Liu iJADE eMiner - A Web-baed Mining Agent based on Intelligent Java Agent Development Environment (iJADE) on Internet Shopping To appear in Lecture Notes in Artificial Intelligence series Springer-Verlag 2001

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Figure 2 Intelligent shopping scenarios for I ) Internet Shopping (e-shopping) 2) Mobile WAP-based Shopping (m-shoppmg)

TABLE I

A In WAP phone amp WAP gateway (m-shopping)Client browser (e-shopping) CRD I RFS I 25 I 73 I 310 1 305

B In Cyberstore (both m-shopping and e-shopping) FANS I 225 I 1304 I 320 1 2015 FPSS I 3120 1 3311 I 4260 I 4133

C In WAP phone amp WAP gateway (m-shopping)Client browser (e-shopping) PDS 1 310 I 335 I 320 I 330 PES I 53 I 102 I 251 I 223

Total RTT I 3733 I 5125 I 5461 I 7006

Product Selection (PS) Test Unlike the RTT test in which objective figures can be easily

obtained the PS test results rely heavily on user preference In order to get a more objective result a sample group of 40 candidates was invited for system evaluation In the test each candidate would buy one product from each category according to hidher own requirements For evaluation they would browse around the e-catalog to choose a list of the best five choices (L) which fit hidher taste In comparison with the top five recommended product items (i) given by the hzzy shopper the Fitness Value (FV) is calculated as follows In the calculation scores of 5 to 1 were given to correct matches of the candidates first to fifth best five choices with the fuzzy shoppers suggestion For example if out of the five best choices selected by the customer products of rank no I 2 3 and 5 appear in the fuzzy shopper recommended list the fitness value will be 73 which is the sum of I 2 3 and 5 divided by 15

Four different product selection schemes were adopted 1) Simple product selection (using product description matching - traditional technique) 2) Product selection based on FFBP neural network training 3)Product selection based on fuzzy product

description - no network training is involved 4) m-shopper - product selection based on fuzzy-neural training

The corresponding Fitness Values (FV) and the degree of improvement (against the traditional technique) under the eight different product categories are shown in Table 11 Table I I - Fitness value for the eight different product categories

under different product selection schemes

I Fitness Value FVK IK Imorovement)

CONCLUSION In this paper the author proposes an innovative mobile shopping solution based on fuzzy-neural based intelligent agents hopefully it will open a new era of intelligent-based mobile e- Business in the new millennium References [ I ] [2] Voyager URL httpwwobjectspacecomvoyager [3] [4] Jango URL httnwianrocom [5]

Aglets URL httpwwwtrlibm CO jpiagletsl

Bargain Finder URL httobf cstar accombf

R S T Lee and J N K Liu Fuzzy Shopper - A fuzzy network based shopping agent in E-commerce environment In Proc of MAMA2000 December 1 1 -13 Wollongong Australia 2000

[6] R S T Lee and J N K Liu iJADE eMiner - A Web-baed Mining Agent based on Intelligent Java Agent Development Environment (iJADE) on Internet Shopping To appear in Lecture Notes in Artificial Intelligence series Springer-Verlag 2001

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