agents’ roles in b2c e-commerce - semantic scholar · context, the use of software agents as...

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Agents’ roles in B2C e-commerce ∗

Luigi Palopoli a,∗∗ , Domenico Rosaci b andDomenico Ursino b

a DEIS,Universita della CalabriaVia P. Bucci, 87036 Rende (CS), ItalyE-mail: [email protected] DIMET,Universita Mediterranea di Reggio CalabriaVia Graziella, Localita Feo di Vito,89060 Reggio CalabriaE-mail: {domenico.rosaci,ursino}@unirc.it

The rapid evolution of the Internet from a generalinformation space to an electronic market space pro-vides the users with the possibility to navigate amongthousands of Web sites for comparing products andmerchants, for making their purchases or for obtainingsome desired services. However, from the user’s view-point, such a navigation often requires an high cost interms of time to spend on the Web to perform a satis-fying comparison of the various alternatives. From thesupplier’s viewpoint, it is needed to propose productsin a suitable way to customers, taking into account thetypology of each customer, her/his preferences, habits,etc.

In this context, generally denoted as Business-to-Customer (B2C) e-commerce, the use of softwareagents as mediators in e-commerce activities seems tobe particularly promising.

In this paper, we describe and analyze the variousroles agents have assumed in B2C e-commerce appli-cations. Furthermore, we propose a Consumer BuyingBehaviour model, called E2-CBB, that considers newemergent issues, as the capability to solve semanticheterogeneity, and the adaptive presentations of Webstores. By using such a model, we classify and com-

*NOTICE: this is the authors version of a work that wasaccepted for publication in AI Communications. Changesresulting from the publishing process, such as peer review,editing, corrections, structural formatting, and other qual-ity control mechanisms may not be reflected in this doc-ument. Changes may have been made to this work sinceit was submitted for publication. A definitive version wassubsequently published in AI Communications, [VOL 19,ISSUE 2, (January 2006)]

**Corresponding author

pare a number of agent-based approaches for manag-ing B2C e-commerce, proposed in the literature in thelast ten years.

Keywords: B2C E-commerce, intelligent agents, multiagent systems

1. Introduction

Nowadays, the Internet is evolving from a gen-eral information space to a market space present-ing a large variety of commercial services, rang-ing from electronic Web-stores to auctions, on-line booking and other services [1,157,29,94]. Therapid growth of networking systems is stimulatingan ever-increasing number of businesses to parte-cipate in e-commerce worldwide [125]. Users havethe possibility to navigate among thousands ofWeb sites for exploring desired sources and makingtheir purchases. However, for performing the var-ious tasks typically involved in e-commerce trans-actions, a customer has often the necessity tospend a large amount of time on the Web. Indeed,a user has to (i) identify those products appearingsuitable to satisfy her/his needs; (ii) compare thedifferent offers for products of interest; (iii) per-form a negotiation, if the product’s price is notfixed [59,58,75]. In addition, a customer is oftennot able to perform a satisfying comparison of thevarious available alternatives, since the followingcomplex problems must be dealt with:

– The increasing number of electronic market-places makes it impossible for customers tokeep track of the ever-changing offer, demandand price situation.

– Some activities, as the participation to auc-tions, often need a continuous on-line presenceof the customer for monitoring offers’ evolu-tion.

– Due to the heterogeneity of Web sources, aproduct, or a product category, may have dif-ferent identification in different Web sources,making comparisons really difficult.

AI CommunicationsISSN 0921-7126, IOS Press. All rights reserved

2 L. Palopoli et al. / Agents for B2C e-commerce

– Customers are not properly assisted for tak-ing their decisions: they would often need anexpert opinion for making the right decision.

On the other hand, electronic suppliers havesimilar problems: they have the necessity to pro-pose their products to customers in the most suit-able way, taking into account their preferences,habits, etc. as well as competitors’ offers.

The previously described issues are typicalof the so called Business-to-Customer (B2C) e-commerce; most of them lead to the necessity forhumans to find new ways of being simultaneouslypresent at as many e-commerce sites as possible, inorder to carry out profitable transactions. In thiscontext, the use of software agents as mediatorsin e-commerce activities has been indicated as apromising direction to follow [58,105].

A software agent is a computing entity capa-ble of (i) perceiving dynamic changes in the envi-ronment, (ii) autonomously performing some tasksdelegated by users, (iii) possibly communicatingand cooperating with other agents operating in theenvironment. Agents are considered useful in elec-tronic business because they can solve problemsin an adaptive, goal-driven way, acting as proac-tive catalysts [83]. They can retrieve information,monitor other trading agents, watch for poten-tial opportunities, establish connections and nego-tiate with many potential partners at once. More-over, they can automatically: (i) provide users hav-ing special interests with target information [104],(ii) filter information [90], (iii) integrate informa-tion from heterogeneous sources [81,82], (iv) de-rive stereotypical behaviour and, finally, (v) regu-late transactions for more efficient business inter-actions [67].

The important role of Software Agents in theB2C e-commerce context is witnessed by the largenumber of agent models and architectures pro-posed in the literature [130,58,137,138] as wellas by the large variety of real e-commerce appli-cations adopting agent technologies within theirsites. In this respect, it is worth mentioning thefirst Trading Agent Competition (TAC) held inBoston in July 2000; such a competition providedbenchmark problems for the complex domain ofe-marketplaces [53].

The purpose of this paper is to both provide adescription and to illustrate a taxonomy of mostof the agent-based approaches to B2C e-commerceproposed in the literature. A number of other

surveys on this subject have been produced inthe past. In particular, the work [59] representsthe first attempt to review agent-based systemsfor B2C e-commerce. Other important works de-scribing the state of the art in such a contextare [154,98,179,143,11,19,74,96,101,112,65,70,61].With respect to these surveys, the present paperprovides the following contribution:

– Due to the rapid evolution of the agent tech-nology, some important issues have been re-cently emerged that are not covered by theaforementioned works, in particular the as-pect of the agent-mediated visit to an e-commerce site. In this paper, we propose ageneral model, called E2-CBB, for represent-ing the customer behaviour in an e-commercecontext; E2-CBB introduces a new phase,called site visiting, besides the other onesgenerally taken into account in the previoussurveys. This allows recent agent-based ap-proaches, dealing with this specific problem,to be classified.

– The site visiting phase mainly concerns theextraction and the organization of informa-tion associated with both the site content andcustomer behaviour in accessing it. Problemssuch as the construction, the managementand the exploitation of a customer profile, aswell as the adaptation of the site presenta-tion to different visiting customers, are stud-ied in this context. From our point of view,the site visiting phase is central in the modernagent systems, since the other phases of thee-commerce process, such as the identificationof the most suitable products and merchants,can suitably exploit the information extractedduring this phase.

– We systematically analyze a number of recentsystems and build a taxonomy of them basedon the proposed categorization.

– Finally, we have taken into account that thesubject underlying this survey involves con-cepts belonging to quite different fields, suchas artificial intelligence, software system en-gineering, economics and so on. As such, weextensively describe various concepts that arenot strictly pertinent to the computer sciencefield (as, for instance, the various types ofauction protocols) and refer several authorita-tive work about e-commerce, considered fromthe economics viewpoint. In addition, we give

L. Palopoli et al. / Agents for B2C e-commerce 3

some basic concepts about agents, by provid-ing also an agent taxonomy, that can be use-ful to better understand the various agent ty-pologies we introduce in this survey.

The plan of the paper is as follows. Some basicconcepts about e-commerce are given in Section 2.Section 3 is devoted to introduce software agents.Agent-based approaches to B2C e-commerce appli-cations are discussed in Section 4. Section 5 dealswith the new emergent role of the Semantic WebServices. Finally, in Section 6, some conclusive re-marks on the future perspectives of the research inagent-based B2C e-commerce are drawn.

2. E-Commerce: from B2C to B2B

Generally, the term e-commerce denotes anybusiness activity carried out electronically ratherthan face-to-face [157,174]. Since entities involvedin e-Commerce activities are basically firms (gen-erally denoted as businesses), customers and gov-ernments, six kinds of e-commerce have been de-fined in the literature [48]: Business-to-Business(B2B), Business-to-Customer (B2C), Business-to-Government (B2G), Customer-to-Customer (C2C),Government-to-Customer (G2C) and Government-to-Government (G2G). However, the two majorforms of e-commerce are B2C e-commerce, inwhich businesses sell directly to customers, andB2B e-commerce, in which businesses carry outtheir tasks with other businesses [151].

For instance, consider a customer who visits amegastore, buys some products and pays for them:in this case she/he is carrying out a B2C com-merce activity, since the involved entities are aBusiness (the megastore) and a Customer. Anal-ogously, when the customer carries out the sameactivities on an e-shop, we say that she/he carriesout a B2C e-commerce activity. On the other hand,the megastore presumably buys all the goods itsells from other businesses: such a task is exactlya B2B commerce activity, since both the entitiesinvolved in the underlying transactions are busi-nesses. Analogously, if an e-shop carries out itsbuying activity by exploiting electronic enginesprovided by other selling companies, we say thatit performs a B2B e-commerce activity.

Since it is easier to reach customers through theInternet than to set up communication channels

among different organizational networks, B2C e-commerce has grown faster than B2B e-commerce.Such a trend has changed in the last five years be-cause firms have understood that huge costs can besaved by carrying out their inter-business transac-tions directly via Web. As a consequence, they arepaying growing attentions to B2B e-commerce ap-plications and, indeed, B2B e-commerce is nowa-days expected to grow faster than B2C e-commerce.The U.S. Department of Commerce estimates that,in the year 2004, between $4 billions and $14 bil-lions on-line sales will be performed in the B2Ce-commerce area in the US; since the number ofInternet users grew enormously in the last years(e.g., from 100 to 300 millions between 1997 and2000), it is possible to anticipate a significantgrowth of B2C e-commerce in the next years.

From an agent-system viewpoint, there aremany differences between B2B e-commerce andB2C e-commerce, which deeply influence the char-acteristics of the corresponding agents. The mostrelevant of them are summarized by the followingconsiderations:

– B2C e-commerce involves only a subset ofcompany operations, basically those businessaspects dealing with marketing and sellingto consumers; instead, B2B e-commerce dealswith a superset of B2C e-commerce, encom-passing several aspects of doing business,from advertising and marketing to sales, or-dering, manufacturing, distribution, customerservice, after-sales support, etc. [147].

– The major factors driving the implementa-tion of B2C e-commerce include increasingrevenues, developing new sale channels andnew customer groups, obtaining competitivepressures and extending global reach [1]. Viceversa, the major factors driving the imple-mentation of B2B e-commerce include: im-proving business processes, improving sup-plier and customer relations, increasing prof-itability, saving costs, increasing distribution,pressing from customers and competitors.

– Finally, while interoperability is not a majorissue for B2C e-commerce, it plays a key rolein B2B e-commerce. With regard to this, it isworth pointing out that, in B2C e-commerce,interaction with customers is generally carriedout via a Web browser, whereas, currently, inB2B e-commerce, no form of interaction hasbeen adopted as a “de facto” standard.


This survey is manly focused on agents con-ceived for performing B2C e-commerce activities.

3. Agent technology: a way for reducing userwork

Agent technology has largely developed in thelast few years, evolving rapidly along a numberof typologies such as intelligent agents, mobileagents, collaborative agents, etc. Such a large de-velopment has mostly been due to the necessity ofassisting and simplifying the interaction betweenusers and computers.

When users directly perform everyday activi-ties over the net, such as managing e-mail, ac-quiring news, surfing the Web, selecting productsfrom e-commerce sites and so on, they have bothto explicitly start all such tasks and to monitorall involved events. In order to simplify such acomplex interaction between users and comput-ers, autonomous software agents [102,71,18] can beadopted. These are software modules which collab-orate with users by acting as personal assistants.The idea of exploiting agents for carrying out someof the user activities, more specifically those tooonerous to be performed through a direct interac-tion with a computer, emerged in some pioneeris-tic works like those of Alan Kay [77,78], BrendaLaurel [92,93] and Nicholas Negroponte [114,115]that advocate agents as personal assistants actingon user’s behalf in the cyberspace. In the last fewyears, several agent theories, languages and archi-tectures have been developed and a lot of defini-tions have been provided for explicating the useof the word “agent”[152,142,141,103,60,35,50,173,175,124]. We try to synthesize the requirementsexpressed for agents, as follow:

Definition 3.1 A software agent is a computing en-tity capable of perceiving dynamic changes in theenvironment and, consequently, of autonomouslyperforming user delegated tasks which may changethat environment, possibly by communicating andcooperating with other agents operating in the en-vironment.

Moreover, on the basis of the various definitionscited above, some properties can be introduced forcharacterizing agents’ behaviour [50,117]. In moredetail, an agent is:

– Reactive, if it is capable of answering to envi-ronment changes.

– Autonomous, if it can operate on its own.– Deliberative, if it owns an internal symbolic

reasoning model.– Pro-active, if it is able to take the initiative.– Continuous, if it is a continuous running pro-

cess.– Adaptive, if it adapts its behaviour on the ba-

sis of its own experience.– Mobile, if it is capable of transferring itself

from one machine to another.– Flexible, if its actions are not fully pre-defined.– Sensible, if it has a believable personality and

a representation of emotional states.– Social, if it is capable of communicating with

other agents.– Collaborative, if it shares its own knowledge

with other agents in order to pursue its ownor shared tasks.

According to [117], software agents can be clas-sified as follows:

– Interface Agents. Maes defines interface agentsas “computer programs that employ artificialintelligence techniques to provide active assis-tance to a user with computer-based tasks”[102]. In particular, an Interface Agent can beseen as a learning interface, operating in ap-plications exploited by the user, which grad-ually builds a knowledge base of the actionsthe user carries out in certain situations. It isdesirable that such an agent exhibit a learn-ing potential to improve its decision-makingmethods.

– Mobile Agents. Mobile Agents are software en-gines capable of moving around networks suchas the Internet, interacting with other hosts,gathering information on the behalf of theirowners and returning any interesting informa-tion they found [117]. Mobile agents stop theirexecution when they move around a network;they continue their execution only when theyarrive at an host capable of re-starting them.The necessity of suspending their executionoften represents a serious limitation here.

– Collaborative Agents. A collaborative agentis able to communicate with, and react toits environment when other agents operatetherein. Collaboration exists when the actionsof an agent achieve not only its own goals,


but also those of the other agents present inthe environment. Agent collaboration is ob-tained by singling out, in an agent community,the agents potentially suitable for cooperat-ing, and by sharing knowledge among them[24]. This can be obtained, as an example,by computing similarities between agent on-tologies, thus forming clusters of agents hav-ing similar interests and behaviour (see, forinstance, the approach proposed in [139]).In order to make it easier, an agent Knowl-edge Interchange Format (KIF) can be ex-ploited (the Knowledge Query and Manipula-tion Language - KQML [49] is an interestingexample of a KIF).

– Reactive Agents. Some works, such as [22],tried to show that an intelligent behaviour inagents can be realised without symbolic rep-resentations of the agent environment, whichresulted in the definition of reactive agents.These are particular agents which do not owninternal symbolic models of their environ-ment. Instead, they react to a stimulus or in-put caused by some state or event in their en-vironment. Reactive agents typically have ahigh level of autonomy as they simply reactreliably to events in their environment.

– Hybrid Agents. As shown in [117], all previ-ously introduced agents are characterized bysome strengths and weaknesses and no agentclass is perfectly suited for each given task.Therefore, it appears particularly interestingto exploit the characteristics of several agentclasses at the same time, (e.g., autonomousand cooperative agents) for developing agent-based applications. This is the classical sce-nario for exploiting hybrid agents. These aresimply systems consisting of different agentclasses, together with an agent control unitallowing each agent class to effectively inter-operate with the other ones.

– Heterogeneous Agents. Whereas hybrid agentsystems consist of agents belonging to dif-ferent classes, heterogeneous agent systemsconsist of of agents (belonging to either thesame class or different classes) that have theirown knowledge expressed in different formats(e.g, XML ontologies, E/R knowledge bases,etc.) and inter-operate by means of controlleragents [136,14,140]. These last make the in-teroperability easier by exploiting standard

agent communication languages (ACL) andmethods.

– Information Agents. These agents usually ex-ploit Web search engines, such as Google, Ya-hoo and Altavista, in order to support a navi-gator in the management of the huge amountof information found over the Internet.

– Holonic Agents. An holonic agent [51] con-sists of a structured hierarchy of sub-agents(called holon) each possibly including furthersub-agents. One sub-agent of the holon, calledhead agent, moderates the activities of all theother sub-agents.

In this paper we are interested in surveyingabout software agents specifically operating in e-commerce environments. In the next sections, wewill concentrate our attention on them.

4. Agents supporting B2C e-commerce activities

There are several reasons which justify the ex-ploitation of agents in e-commerce applications;the most relevant of them are:

– Intelligence. Agents can exploit learning tech-niques for taking advantage of their own expe-rience in order to improve their performances.

– Personalization. An agent assisting an Inter-net user can store an internal representationof her/his preferences, i.e. a profile, and cantune its behaviour according to it.

– Social behaviour. Agents are able to interactwith each other. Such a communication capa-bility may allow them to negotiate, e.g. overprices, services and transactions.

– Autonomy. In the previous section we havepointed out that the most relevant feature ofan agent is the ability to work independentlyon the human intervention. In e-commerce ap-plications, this ability can be exploited for del-egating to agents several tasks that would re-quire a relevant time cost for the user, e.g. forautomatically searching the most convenientproducts, for suitably negotiating with sellers,and so on.

The points we have listed above allow to con-clude that agents can be advantageously exploitedas mediators in B2C e-commerce. In order to ex-plore and categorize the various agent-based ap-proaches to e-commerce proposed in literature, it


is useful to have a common reference model repre-senting the underlying e-commerce activities. ForB2C e-commerce, it is possible to directly refer tothe buying behaviour of the customers and, thus,to use one of the most classical models proposed inthe traditional market theory. We briefly describeit in the next subsection.

4.1. Modeling Consumer Buying Behaviour

4.1.1. Existing CBB ModelsAgents operating in e-commerce activities can

be seen as mediators between the actual subjectsinvolved in this context, i.e. customers and busi-nesses.

It is useful to consider the behaviour of an agentin the context of a uniform framework representingthe various phases of its activity. Traditional mar-keting research has developed many descriptivetheories and models that attempt to capture theconsumer behaviour in finding, buying and usinggoods and services. The research about the Con-sumer Buying Behaviour (CBB) deals primarilywith B2C e-commerce although several CBB con-cepts are still valid for B2B and C2C e-commerceprocesses. Below, we describe three proposals formodeling the consumer behaviour within an e-commerce site:

– The Traditional CBB models (T-CBB). Thereare several models that were developed in theCBB context, such as the Nicosia Model [116],the Howard-Sheth Model [64], the Engel-Blackwell Model [45], the Bettmann Informa-tion Processing Model [15] and the AndreasenModel [5]. They differ in various aspects; how-ever, as noticed in [59], all of them proposesix relevant steps in the consumer buying be-haviour. These steps, graphically representedin Figure 1, are listed below:

1. Need Identification. In this stage the con-sumer is stimulated to become aware ofsome unmet need. For instance, considerthe case of a customer that is interestedin a certain category of books. Agents cancontinuously monitor the Web and advertthe customer when a new book of thatcategory is available. This stage is calledProduct Recognition in the Engel-Blackwellmodel [45].

2. Product Brokering. Once a consumer hasidentified a need to satisfy, she/he has tofind what to buy through a careful evalu-ation of the various products possibly sat-isfying that need. This requires a compari-son of product alternatives based on someconsumer-provided criteria. At the end ofthis step, a set of products, usually calledthe consideration set, capable of satisfyingthe consumer desires, has been identified.

3. Merchant Brokering. In this step, the con-sideration set is combined with merchant-specific alternatives based on consumer-selected criteria (e.g, availability, price, de-livery time, warranty, reputation, etc.) forhelping the consumer to determine whomto buy from.Note that some classical marketing mod-els, such as the Nicosia model [116], mergeboth product brokering and merchant bro-kering phases into a unique search evalu-ation phase, whereas other authors, e.g.,[45], enucleate in both the brokering phasestwo orthogonal stages called InformationSearch and Evaluation of Alternatives.

4. Negotiation. During this step, the variousterms of the transaction as, for instance,the price, are determined.In real-world situations, a negotiation stepoften increases transaction costs in tooan onerous way for both consumers andmerchants. Furthermore, there often mightbe impediments, notably time and spaceconstraints, to exploit negotiation mecha-nisms. These impediments mostly disap-pear in the Internet world.The benefit of negotiating the price of aproduct instead of fixing it is that it re-lieves the merchant from needing to de-termine the value of the good a priori[105]. Rather, the price is dynamically de-termined by the marketplace. The durationand complexity of the negotiation strictlydepend on the market.Some traditional CBB models do not ex-plicitly identify a negotiation phase, butsome of them [116,45] consider a Choice ora Decision phase that is comparable withthe negotiation process described above.

5. Purchase and Delivery. This step can ei-ther represent the termination of the ne-


POST-SALE

NeedIdentification

ProductBrokering

Vendorbrokering

Negotiation PurchaseandDelivering

ProductService

Publicity SearchEngine

Portal E Auction- -E Shop E-mailProfiling

PRE-SALE SALE CARRYING OUTSaleModel

CBBModel

Examples

Fig. 1. Consumer Buying Behaviour Model

Customer

Welcome inthe shop

Registration Profilesmanagement

Open theproduct catalog

Special offerSearch for aproduct

Choose/look atthe item

Extern link tothe product

Transfer inshopping cart

Shopping cartadministration

Negotiation

Choose paymentand shippmentmethod

2d registration

Send order

Sales orderdisplay/send

Confirmation ofthe sales order

First contact

Information

Decision

Order

Post salet

Configurator

Fig. 2. e-commerce Customer Buying Process Model

gotiation phase or can occur sometimes af-terwards. It is possible, in some cases, thatpayment or delivery options can influenceproduct and merchant brokering.

6. Service and Evaluation. This step involvesthe final activities of the buying process,such as product service, customer service,evaluation of the overall process and so on.

In Figure 1 we highlight some e-commerceprocesses, such as publicity, search engines,

portals, auctions, etc, that might support thesix steps we cited above.

– The Extended CBB model (E-CBB). [61] pro-poses to extend the traditional CBB modeldescribed above in order to cover the buyercoalition formation behavior. Indeed, it ob-serves that customers, after having chosenthe product to buy in the product broker-ing stage, before choosing the most suitablemerchant in the merchant brokering stage,may interact with other (similar) buyers to


form a buyer coalition. Such a coalition groupscustomers cooperating with each other in or-der to achieve a common goal. If each cus-tomer has an associated software agent, thebuyer coalition formation can be viewed asa set of software agents that communicatewith each other for performing a commontask, e.g.,trying to approach the merchantwith a large order in order to obtain a lever-age. E-CBB does not include the two laststages of T-CBB (purchase and delivery, prod-uct service and evaluation) since, according tothis model,these stages do not present aspectsstrictly requiring the use of the agent technol-ogy.

– The CBP model. The main limit of the pre-vious models is that the various steps mightoverlap and reorder differently. In order toovercome such a limit, Bohm, Felt and Uellner[16] propose a different model focusing on con-sumer buying behaviour within e-commercesites. This model, called e-commerce Cus-tomer Buying Process(CBP) and depicted inFigure 2, allows to decompose, in a more de-tailed way than the models we have previ-ously presented, a buyer process action intophases (like first contact, information, etc.)and processes (like welcome to shop, Registra-tion, etc.).

4.1.2. A new CBB modelAfter determining the most suitable merchant

for buying the desired products, before beginningnegotiation, the customer often visits the merchantsite, in order to know more details about the prod-uct offer or to find other offers related to prod-ucts of interest. Agents can suitably mediate sucha stage, as follows:

– Customer’s viewpoint. An agent can retrieve,on behalf of its owner, the most interestinginformation about the selected products. Fur-thermore, it can automatically explore themerchant site for determining other productsof interest, and can give advice to the cus-tomer about their presence in the site.

– Seller’s viewpoint. Some seller agents can bepresent on the merchant site for providingthe customer with the most suitable presen-tation, w.r.t her/his profile. For instance, aseller agent may recognize that the customeris not interested in multimedia presentations

and, consequently, may decide to propose alight presentation, without animations, ap-plets, etc. Such a capability is related to thewell-known issue of the adaptivity in the Websites.

Both from the customer’s and the seller’s view-point, it is possible to note that the operations ex-ecuted by a unassisted customer, whenever she/hebuys a product or a service from a B2C e-commerce site, can be roughly grouped into twomain activities, namely: (i) the exploitation of aWeb search engine (such as Google) for finding po-tentially interesting sites; (ii) the use of a Webbrowser for navigating through the pages of a sitethat the customer has judged to be interesting.These two activities are important for the cus-tomer, that has to carry out them for searchinginteresting goods, and also for the seller, that ininfluencing is interested the customer’s search forattracting her/him to its own site and products.While a large variety of tools already exists overthe Internet for helping a customer in the search ofpotentially interesting sites, only a relatively lim-ited number of supports are available for assist-ing her/him in the navigation within an interestingsite.

One of the most common type of such sup-ports are recommender systems [145,146] whichhave been conceived for helping the customer ofan e-commerce site in purchase activities. In orderto carry out its task, a recommender system pro-vides the customer with the list of those productsand services available on the site and appearing tobe the most successful, according to the opinionand past purchases of other customers. Moreover,it may analyze the past behaviour of the customeron the site for predicting those goods which, pre-sumably, will be of interest for her/him in the fu-ture. Last, but not the least, it could classify acustomer according to some parameters and mightprovide offers taking into account this classifica-tion. In order to perform these activities, a recom-mender system must process available data. Such atask can be carried out by exploiting various com-putation tools such as cross-sell lists and informa-tion filters. Note that, as such, recommender sys-tems are concerned with the first four stages of theCBB model.

Another important tool that is provided in alarge number of e-commerce sites is a (often sim-ple) handler of customer profiles, exploited for


proposing personalized offers. With such tools,profile construction usually needs a relevant inter-vention of the customer who is required to pro-vide information about her/his interests and de-sires; the profile is usually just intended as a sum-mary of this information. This profile constructionmethod requires the customer to spend a certainamount of time for constructing and/or updatingher/his profile. In addition, these systems make of-ten quite a straightforward management of user’sinterests as stored in the profile.

When a customer accesses an e-commerce site,generally she/he must personally search the issuesof interest through the site. As an example, con-sider the bookstore section of Amazon.com: when-ever a customer looks for finding a book of inter-est, she/he must carry out an autonomous per-sonal search of the book throughout the pages ofthe site. For improving the effectiveness of the e-commerce services, it would be necessary to in-crease the quality of the interaction between thesite and the customer, on the one hand, and to ex-ploit richer customer profiles, taking into accountdesires, interests and behaviour, on the other hand.In order to obtain these results three main issueshave to be taken into account.

First, a customer can access many e-commercesites; a faithful and complete customer profile canbe constructed only taking into account the con-tents of all these sites. In order to construct sucha customer profile, it may be necessary to recog-nize semantically similar concepts represented bydifferent constructs in the various sites and to rep-resent them by a unique construct in the customerprofile.

Second,it appears necessary to represent,in thecustomer profile,not only her/his interests, butalso her/his behaviour in accessing interestingitems. This issue has been considered in someworks in the Information Systems field ([14,28,165,23]). As far as the e-commerce research is con-cerned, a solution for handling the large hetero-genity of product formats has been found in theexploitation of intensional descriptions of productcharacteristics, generally referred to as customerontologies [118].

Third, for a given customer and e-commercesite, it would be useful to compare the profile ofthe customer with the offers of the e-commercesite for extracting those issues that probably areof interest for the customer. Existing techniques

for satisfying such a requirement are mainly basedon the exploitation of either log files [26,180] orcookies [126]. Techniques based on log files are ca-pable of registering some information about theactions carried out by the customer on accessingthe site; however, they are not capable of matchingcustomer preferences and site contents. Vice versa,techniques based on cookies are able to carry out acertain, even if primitive, matching; however theyneed to know and exploit some personal informa-tion that a customer might consider private.

it appears mandatory to solve the above issuesconcerning the management of the site visits. Infact, by using information on customer navigation,it is possible to support the search of those prod-ucts appearing to be the most promising accordingto the customer past behaviour, as well as to sup-port the merchants to propose interesting offers tocustomers. As a consequence, customer past be-haviour is a potential input for the product broker-ing and the merchant brokering phases. Moreover,the construction of customer profiles, that can beperformed in the site visiting stage, may be usedfor comparing different customers and for discov-ering similar tastes and preferences. Such an infor-mation could be exploited for supporting the buy-ing coalition formation and the negotiation phases.

None of the aforementioned CBB models includesuch a site visiting stage; however, several recentlyproposed agent-based systems provide some capa-bilities useful to help customers and sellers to man-age this stage.

In the following of this paper, we will use a fur-ther extension of the E-CBB model, covering alsothe site visiting stage, as the reference model of theConsumer Buying Behaviour for our e-commerceagent classification. We choose the E-CBB modelsince it is more schematic (though less detailed),and therefore more suitable for a classification,than the CBP model. Hereafter, we will denote ourextended E-CBB model simply as E2-CBB model.It presents the following six stages (see Figure 3):(a) need identification; (b) product brokering; (c)buyer coalition formation; (d) merchant brokering;(e) site visiting; (f) negotiation. The meanings ofeach of these stages is that specified in the abovediscussion. In this figure, we have represented bysolid lined the chronological path of the customerin performing the various stages, while we haveused dotted lines for representing relationships be-tween stages. The figure highlights that, on the one


hand, the visit of the site strongly influences otherstages as need identification, product brokering,merchant brokering and buyer coalition, since thisvisit contributes in defining those customer pref-erences that will be exploited in the other stagesand, on the other hand, the site visit itself is influ-enced by the other stages because the site presen-tation may strictly depend on the choices made inthe past by the customer in those stages.

4.2. A taxonomy of agent-based approachessupporting B2C e-commerce applications

In the past, various agent-based approacheshave been proposed for supporting e-commerce ac-tivities. In the following, we analyze them into de-tail, according to their relevance w.r.t. the vari-ous steps of the E2-CBB model. As a synopticalview of such an analysis, we propose the taxonomyillustrated in Figure 4.

At the end of this Section, we have includeda sub-section concerning two additional issuesthat, in our opinion, are worth of being analyzed,namely (i) support to novice users in configuringa complex product and (ii) the recommendationstrategies that do not suggest just similar productsbut also take care of diversity

4.2.1. Agents supporting Need IdentificationBelow we overview some existing agent systems

that help to anticipate consumers’ needs in an au-tomatic way. Most of them are very simple; theironly purpose is to inform the customer when agood of interest becomes available.

– Amazon. To keep customers interested inAmazon [3](see Fig. 5), this firm offers twoforms of e-mail-based services, namely the“Eyes” program and “Editors Service”, to itsregistered customers. The “Eyes” program isa personal notification service in which cus-tomers can register their interests for a partic-ular author or topic. Once registered, they arenotified each time a new book written by oneof their favorite authors or discussing topicsof their interest becomes available.The “Editors Service” program provides edi-torial comments about featured books via e-mail. Many full-time editors at Amazon readbook reviews, examine customer orders andsurvey current events to select the featuredbooks. These and other editors employed by

the firm provide registered users with e-mailupdates on the latest books they have beenreading.The “virtual” services described above bene-fit of the automatic user identification, per-formed by the software agent.

– Fastparts. The FastParts service at the site[47] provides online trading facilities for reg-istered members. Among the others, the Au-toWatch agent allows members to list goodsthey need that are not currently available onthe site. The agent will notify the customer bye-mail when those goods are posted for sale.

– Firefly. The Firefly agent [148] helps cus-tomers to find products of interest by com-paring product rates of a shopper with thoseof the other customers and identifies groupsof homogeneous buyers on the basis of theirrates. Such a shopper grouping is exploitedby the Firefly agent for recommending toeach customer those products which receiveda high rate from the other customers of thesame group but which she/he did not consideryet.

– Classified 2000. Excite’s Classified 2000 [34]is a very simple tool supporting need identi-fication. It behaves as follows: the customercan specify what she/he is looking for by priceand type. A tool called Cool Notify then sendsan e-mail message to the customer when somegoods having the specified features have beenlocated on the Excite site.

– National Instruments. At the site [149], it ispossible to create a personal profile by fill-ing in some fields of a form and registeringas a member. The system provides each regis-tered member with e-mails about National In-struments calendar event notification, closingstock prices and news releases.

– SERSE. In the system SERSE [164], a noti-fication agent receives XML messages from aNotification Server and parses them. It canthen resend the content as an ACL messagevia aPortal Agent. The messages can be thenotification of a new OWL ontology or of newcontent acquisitions. If the notification refersto a new ontology, the Notification Agent ex-tracts the concepts definitions from the OWLfile and populates the system with the routers.It also informs the routers of which agents aretheir immediate neighbours.


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Fig. 3. The E2-CBB Consumer Buying Behaviour Model. Solid lines represent the customer’s chronological path, while dottedlines represent relationships between stages

4.2.2. Agents supporting product brokeringAfter a customer has identified a need, she/he

has to examine the various products available inthe marketplace that might satisfy this need. Sev-eral agent systems have been developed for sup-porting such a task. Generally, they adopt one ofthe following techniques:

1. Feature-based filtering: Products are selectedon the basis of feature keywords. If a cus-tomer is interested in a product with someparticular features (e.g, the category, theprice), its agent specifies them to the sellersite, and obtains all the products with thosefeatures.

2. Collaborative filtering: Products are selectedon the basis of the similarity holding amongstdifferent customers between different cus-tomers. Generally, systems using this tech-nique associate with each customer a profile,storing her/his preferences. If a customer cneeds some recommendations, the agent de-termines all the other buyers having a profilesimilar to that of c, and recommends to c theproducts they considered the most valuable.

3. Constraint-based filtering: Systems using thistechnique allow the customer agents to spec-ify some constraints on the desired products(e.g., the price range, the delivery time, etc.)and return all the products satisfying theseconstraints.

Below we describe some agents supporting prod-uct brokering. In Figure 4 we classify them on the

basis of the adopted technique (feature-based, col-laborative filtering or constraint-based).

– Amazon. Amazon’s recommender tool, calledCustomers Who Bought, provides an infor-mation page for each book, showing somedetails and purchase information. When acustomer visits the site and selects a bookfrom the catalog, two different recommenda-tion lists proposed, she/he gets. The first oneincludes books frequently purchased by othercustomers who bought the selected book.The second one includes authors whose booksare frequently purchased by customers whobought works by the author of the selectedbook.

– CDNOW. Also CDNOW [31] exploits a rec-ommendation tool, called Album Advisor,that performs some different tasks. The firsttwo are similar to those carried out by theCustomers Who Bought tool in Amazon: acustomer can select the information page of agiven album (resp., artist) and the system rec-ommends ten other albums related to the se-lected one. The third task is called Gift Advi-sor: it allows the customers to type the namesof up to three artists and, after this, generatesa list of artists who are considered similar tothe specified ones.A further feature, called My CDNOW, enablescustomers to set up their personalized musicstore, based on albums and artists they like.


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When a customer buys something from CD-NOW, the purchases are automatically putinto an own it list; the user, on the basisof her/his satisfaction, can also classify pur-chases of the own it list into two categories

called own it and like it and own it and dislikeit. This information is exploited by the sys-tem for further recommendations. A customercan also request recommendations on the ba-sis of her/his past behaviour; when this sup-


Fig. 5. The Amazon’s need identification tool

port is required, the tool predicts six albumsthe customer might like based on what she/healready purchased.

– Drugstore. Drugstore [43] provides its cus-tomers with a recommendation tool called Ad-visor, that allows them to indicate their pref-erences when they desire to buy a product ofa certain category. For instance, a customerwilling to buy from the category cold and fluremedies, can indicate the symptoms to re-lieve (e.g., runny nose and sneezing), the de-sired form for reliefs (e.g., caplets) and the agecategory (e.g., adult). After that the customerhas provided the above information, Advisorreturns a list of recommended products.

– eBay. eBay [44] is an auction support system.Two recommendation tools are present at thissite. The first, named Feedback Profile, allowsboth buyers and sellers to give a feedback forupdating the profiles of those they have inter-acted with. The feedback consists of a satis-faction rate that can assume one of the valuessatisfied, neutral, dissatisfied. Purchasers areable to view the profile of each seller, consist-ing of a table with the rates she/he receivedin the past 7 days, in the past month, andin the past six months, as well as an over-all summary. The second tool, called PersonalShopper, allows a customer to indicate items

of interest. Customers can enter a short term(30/60/90 days) and a set of keywords spec-ifying their desires and price limits. The siteperiodically carries out a search over all auc-tions, on the basis of the provided specifica-tions, and informs the customer via e-mailabout the results of the search.

– MovieFinder. At the site [110], three recom-mendation systems are available. The UserGrade system allows customers to registerthemselves at the site and to give a ratebetween A and F to the movies they haveseen. These rates are averaged over all cus-tomers and reported in the site. Furthermore,a rate for each movie is provided by the sitewithin the Our Grade system. The third sys-tem, called Top 10, allows a customer to selecta category from a list and provides her/himwith the top ten movies in that category, asjudged by the site editors.

– Reel. At the site [133], the Movie Matches toolprovides the customer with recommendationsabout movies. Movie Matches behaves in away similar to Amazon’s Who Bought. In par-ticular, recommendations consist of two set ofmovies: the close matches set contains somelinks to the information pages of movies ap-pearing to be very similar to that a customerdecided to buy. The creative matches set con-


tains some links to the information pages ofmovies considered similar, w.r.t. some par-ticular respects; the recommendation systemalso provides motivations for its choices.

– LIBRA. LIBRA (Learning Intelligent BookRecommending Agent) [109] is a new projectaiming at using machine learning techniquesto recommend books to a reader by learningabout her/his interests. The system learns byexamining abstracts and other short descrip-tions of books that the reader has marked asdesirable or undesirable.

– MovieLens. MovieLens [111] requires the userto supply information about the typologies ofmovies she/he likes. Then, it elaborates thesupplied information for providing recommen-dations about other movies possibly interest-ing for the user.

– Alexa. Alexa [2] is a browser plug-in that pro-vides users with recommendations about sitesbased on the Web page they are currently vis-iting. More specifically, it supplies the list ofsites that are someway relevant and related tothat the customer is currently visiting.

– PersonaLogic. PersonaLogic [129] offers to e-commerce companies complete, customized,personalized decision guides for their con-sumers, completely served into their own Websites. PersonaLogic is used within Netscape,AOL, Excite, Lycos, American Express, ZD-Net etc. The PersonaLogic agent, installed inan e-commerce site, allows each customer ofthe site to specify some constraints on prod-uct features. Then, it exploits a constraint sat-isfaction problem (CSP) solving-technique forfiltering out those products that do not satisfyall the specified constraints and returns a listof the products that best match them, orderedby a coefficient representing their matchingdegree. For instance, in searching for a car, theagent asks a customer to differentiate the im-portance of certain factors such as price, size,features, safety, technical aspects and manu-facturer(see Figure 6).

– Firefly. The Firefly agent helps customers tofind products of interest by generating recom-mendations via a “word of mouth” recommen-dation system called Automatic CollaborativeFiltering (ACF). Instead of filtering productson the basis of constraint specifications, likePersonaLogic, Firefly compares product rates

of a customer with those of the other cus-tomers and identifies clusters of homogeneousbuyers on the basis of their rates. Such a clus-tering is exploited by the Firefly agent for rec-ommending to each customer those productswhich received an high evaluation from theother customers of the same group but whichthat customer did not consider yet. This way,Firefly carries out both a need identificationand a product brokering activity.

– Tete-a-Tete. Like PersonaLogic, the Tete-a-Tete agent [59] exploits a CSP technique forassisting customers in their product brokeringactivity. In particular, the customer providesthe system with hard and soft constraints;these are exploited by the agent for identify-ing the most promising products.

– WEBS. WEBS (Web-based Electronic Bro-kering System) [91] consists of some net-worked brokering agents, each handling a par-ticular category of products or services, suchas security trading, books, computer software,etc. For each brokering agent, several sub-agents are provided for handling the varioussteps of the CBB model. In particular, a pro-filing sub-agent is responsible for need identi-fication and product brokering.Each profiling sub-agent associated with aparticular buyer registers her/his preferencesw.r.t. the products of a particular domain.The profile is exploited by the agent for sup-plying information about products presum-ably of interest for the customer. Once infor-mation about a product has been supplied toa buyer, a form-based interface is presented,that allows to require further information, inthe case that the customer is indeed interestedin the product. The buyer reaction to the pre-sented product information is stored in a dia-log file. This is analyzed by the learning com-ponent of the profiling agent for updating theprofile.

– SETA. SETA (SErvizi Telematici Adattativi)[7] is a toolkit for building adaptive WebStores. It exploits some user modeling tech-niques for personalizing the interaction withthe customer. In particular, it generates thecatalogue pages of an e-commerce site on thebasis of the customer preferences.SETA defines a conceptual representation of aWeb store by constructing the so-called Prod-


Fig. 6. The PersonaLogic site

uct Taxonomy, which stores both the def-inition of all products present in the Webstore and their relationships. The ProductTaxonomy is exploited for generating a ratio-nal sequence of Web pages making the cus-tomer navigation easier. The taxonomy is di-rectly mapped in the structure underlying thehypertext forming the Web catalogue. Be-sides this intensional representation of prod-ucts, SETA also stores extensional informa-tion about the various items in a Product DataBase.Furthermore, SETA handles knowledge aboutcustomers, such as personal data, preferencesabout product properties (e.g., quality, easeof use, etc), receptivity, domain expertise, andso on.When a customer accesses the Web store forthe first time, the system requires some per-sonal information to be specified. Then, itconstructs a rough customer profile by match-ing customer personal data with those in-cluded in a stereotype knowledge base. Afterthis, whenever the customer browses the prod-uct catalogue, dynamic user modeling tech-niques are exploited to modify the associatedprofile on the basis of of behaviour customer’s.The Product Brokering activity in SETAis mainly handled by: (i) a Personalization

Agent, which generates the HTML code forthe Web pages to be shown to the customeron the basis of her/his profile, and (ii) a Prod-uct Extractor, which assists the customer inproduct selection by suggesting her/him a listof products, ordered by their probability tosatisfy her/his desires.

– Meng et al.. In [108], a new business agent ar-chitecture is presented, in which the BusinessSpy Agent (BSA) automatically retrieves sup-ply and demand information from e-commenceweb sites. The Supply and Demand Anal-ysis Mechanism (SDAM) uses Natural Lan-guage Processing (NLP) technologies to ex-tract products and trading information.

– InterMarket. InterMarket [87] is a Multi-Agent System in which agents have the au-tonomous decision-making and mobility capa-bilities. They can proactively monitor tradingopportunities, search for trading partners andproducts, and they are able to move to thee-marketplaces through the Internet and canbe initiated from different computer platformsand mobile devices such as mobile phones andPDAs.

– Easishop. Easishop [79] is an intelligent mo-bile shopping system, that allows the user tospecify a composite set of preferences, profileand shopping list information. The agents in


the system are implemented by using agentfactory, that is a cohesive framework that sup-ports a structured approach to the develop-ment and deployment of agent-oriented appli-cations. In order to facilitate interagent datacoordination, the system uses a product ontol-ogy based upon the United Nations standardproducts and services code (UNSPSC).

4.2.3. Agents supporting buyer coalitionformation

In the buyer coalition formation phase, cus-tomers that have made a decision about whatproducts to buy, decide to form a coalition withother customers interested in the same products.This phase is not strictly necessary, and a cus-tomer having completed the product brokeringstage might move directly to the merchant bro-kering stage, without interacting with other cus-tomers. However, in the present agent-based e-commerce systems, the utility for the customers tointeract with one another and to form customercoalitions is considerably emerging, and interac-tion can be efficiently managed with the supportof software agents.

In Multi-Agent Systems literature, the problemof constructing groups of agents that collaboratefor improving their results has been dealt with,according to three different approaches:

– Agent teams. An agent team is a set of agentsthat collaborate to pursue some shared orcommon goals. This approach explicitly re-quires agents to share a sort of joint goal to bereached; thus, each agent of the set is not com-pletely self-interested since the agents’ utilitymeasure is strictly dependent upon the per-formance of the group as a whole. Relevantresearch in this setting is that of [69,156,163].

– Agent coalitions. An agent coalition is a set ofagents that work together to achieve a largergoal. Each agent of the set is completely self-interested and participates in the coalitionsince, by this participation, it receives a higherutility than if it worked alone. Notable exam-ples of research in this setting are [150,144,84].In [177], a buyer coalition formation scheme,called GroupBuyAuction, is proposed. In sucha scheme, buyers form a group based on acategory of items. A buyer can indicate a listof items and can specify a desired reserva-tion price for each item. Sellers can bid vol-

ume discount prices. A particular group leaderagent divides the group into coalitions, se-lects a winning seller for each coalition, andcalculates surplus division among buyers. In[167], a buyer coalition model consisting offive stages is proposed. The stages are ne-gotiation, leader election, coalition formation,payment collection and execution. An imple-mented system using this model is tested ina collective book purchasing context, show-ing how the supplier agent gives a volume dis-count according to the size of the coalition.The most important problem for the coalitionformation technologies is the computationalcomplexity of coalition structure generation.That is, once a group of agents has been iden-tified, the time cost for partioning them inorder to maximize the global social payoff isNP-hard and even finding a sub-optimal so-lution requires searching an exponential num-ber of solutions. In [40], a novel algorithmfor coalition structure generation is presented,that produces solutions that are within a fi-nite bound from the optimal.

– Agent congregation. An agent congregation[21] is a long-lived entity composed by one ormore self-interested agents that interact eachother for satisfying their needs. The conceptof agent congregation is closer to that of agentcoalition than that of agent teams, since theagents of a congregation are self-interested.However, differently from an agent coalition,that usually deals with the accomplishment ofa single task, an agent congregation is a morelong-lived entity that can support agents inthe accomplishment of several tasks. Further-more, differently from the agent coalition, inan agent congregation an agent may not knowthe identities of the other congregation mem-bers when it decides whether to join or not.A congregation serves to allow agents to cometogether and make bilateral exchanges, with-out any joint goal to pursue by the coopera-tion. In [20], the use of agent congregation forsupporting market formation is analyzed. Inparticular, authors illustrate how members ofa multi-agent system self-organize into a setof markets such that agents are able to findsuitable partners while retaining low compu-tational costs.In [168], a congregation formation mechanismfor the electronic marketplace, that extends


the existing transaction-oriented coalitions tolong-term ones, is presented. This proposalfocuses on self-interested agents that tradegoods or knowledge in a large-scale multi-agent system. In this context, congregationsare used as a means of grouping agents for im-proving their cooperation and coordination insuch a way to increase their individual bene-fits.

4.2.4. Agents supporting merchant brokeringDuring the merchant brokering stage, a cus-

tomer compares the various merchants, that mightprovide a product of interest, for choosing themost convenient one. Nowadays, there exist sev-eral price-comparison sites, as [132,131,17,30], thatsimply compare the price of several sellers withrespect to the same product. However, more so-phisticated several agent-based systems thave beenproposed for automating this stage [171,80]. Belowwe discuss the most significant ones:

– BargainFinder. BargainFinder [10] is a shop-ping agent able to compare the price ofa specific product proposed by different e-commerce sites (see Figure 7). Several on-linemerchants cooperate with BargainFinder inthis task in order to both improve their com-petitivity and receive more visibility. A shop-per can use the agent directly at the Bar-gainFinder site, specifying a product, and theagent returns the list of the best offers for thatproduct, selected from all the e-commercesites joined with BargainFinder.

– Jango. Jango [42,68] behaves similarly to Bar-gainFinder. However, in order to make a com-plete comparison of prices, Jango generatesthe price request for a product from consumerusing Web browsers instead of a central site.This way, requests to merchants appear as realcustomer’s requests. Clearly, this behaviour isquite aggressive w.r.t. the merchants.

– Kasbah. Kasbah [32,76] is an on-line multi-agent transactions system developed at theMIT Media Lab, supporting both the merchant-brokering and the negotiation stages. From amerchant-brokering viewpoint, it allows a cus-tomer, wishing to buy a good, to create anagent, to provide it with some specificationsand to send it into a centralized marketplace.Kasbah agents are able to proactively searchpotential sellers for the selected good. The

goal of each agent is to determine the mostsuitable sellers, by satisfying some customer-specified constraints such as the highest (orthe lowest) acceptable price and the date bywhich to complete the transaction. Kasbahincorporates also a mechanism, called Bet-ter Business Bureau that, upon completion ofa transaction, allows both parties (customerand seller) to rate how well the other partymanaged its half of the deal. This way, a sortof reputation is defined for each user; this canbe exploited by the agents in the choice of theparties to consider for the next deals.

– Karacapilidis and Moraitis. In the approachof [75], agents can take the initiative to con-tact their owners in order to start transactionsthat seem to be promising, or can trigger anowner action (e.g., they can inform their mer-chant that a specific offer has been of no in-terest in the market during the last month).This framework assumes a long, or even per-manent, presence of the agent in the e-market.During this time the agent maintains a profileof its associated owner. These agents are ca-pable of refining transaction criteria, handlingincomplete, inconsistent and conflicting infor-mation and, finally, performing a progressivesynthesis and a comparative evaluation of theexisting offers in the virtual marketplace.

– Wang and al. In [172], an agent-mediatedshopping architecture is presented whose aimis to improve the performances of the mer-chant information gathering activity. Gener-ally, since the amount of potentially inter-esting information to examine is particularlylarge and available time is limited, the agentsof a system that supports merchant brokeringcannot perform an exhaustive search for gath-ering merchant information. In this context,the system presented in [172] allows to obtaina trade-off between the quality of the infor-mation gathering process and the time spentfor obtaining it. Authors propose an anytimealgorithm for handling such a trade-off. Any-time algorithms is a class of algorithms thatcan be stopped at any moment, but the qual-ity of the yielded results improves gradually asthe computation time proceeds. They are par-ticularly useful for solving problems where thesearch space is large and quality of the resultscan be mediated. In the proposed trading ar-


Fig. 7. The BargainFinder site

chitecture, the agent autonomously gathersmerchant information and buyers can inquirethe current lowest price at any time. Buyerscan also wait for a price decreasing processwith the elapsing time.

4.2.5. Agents supporting Site VisitingA customer, after having determined the best

merchants whom to buy from, generally visits themerchant site for retrieving more accurate descrip-tions of the goods she/he is interested and/orexamining other offers that might meet her/hisneeds. Information on customer navigation canbe exploited by a suitable agent for constructinga customer profile non-intrusively. Such a profileplays a key role in supporting a customer in thesearch of those products appearing to be the mostpromising ones according to her/his interests, aswell as for helping merchants to propose the mostappropriate offers to the customers. Thus, fromour point of view, the site visiting stage is centralin the agent-supported e-commerce process since,during this stage, most of the information neces-sary for supporting the other stages is collected.

The management of the site visiting stageimplies to deal with some important problems,namely: (i) the presence of semantic hetero-geneities among the various Web sites and (ii) thenecessity to adapt site presentations to customerprofiles. In the following sections, we overviewsome recent proposals for tackling these two prob-lems.

Agent-based approaches handling semantic hetero-geneity In this section we analyze three recentagent-based approaches for B2C e-commerce capa-ble of handling semantic heterogeneities. The firsttwo of them, namely B-SDR and MOMIS systems,can be also used to support the product brokeringstage of the E2-CBB model; vice versa, the thirdsystem, called MORPHEUS, applies also to themerchant brokering stage.

– Mobile agents using MOMIS. MOMIS (Me-diator envirOnment for Multiple Informa-tion Sources) [14] handles both integrationand querying of multiple, heterogeneous infor-mation sources, storing both structured andsemi-structured data. Data source integration


is carried out by following a semantic ap-proach based on Description Logics, cluster-ing and a common data model capable ofrepresenting all involved data sources. Fig-ure 8 depicts the application of MOMIS tothe classical product brokering problem. In-stead of manually performing a lot of querieson several Web pages for retrieving infor-mation about a product (e.g., a car satisfy-ing certain requirements), MOMIS first re-trieves information from various interestingdata sources (e.g., Ford, Volkswagen, FIAT,and so on) and, then, integrate it in a uniquehomogeneous view called Virtual Catalogue.The MOMIS component carrying out sucha task is called SI-Designer [12]. A VirtualCatalogue is, therefore, a tool presenting, ina unified view, product information derivedfrom heterogeneous catalogues. So doing, cus-tomers do not need to interact with multipleheterogeneous catalogues but with a uniqueone, uniformly representing all of them. Af-ter that a Virtual Catalogue has been con-structed, MOMIS extracts a list of productsfulfilling customer requirements from it. [13]extends this approach by introducing mobileagents in the MOMIS framework for allow-ing the autonomous management and the co-ordination of both the integration and thequerying activities. Mobile agents can signif-icantly save bandwidth by moving to the re-sources they need and executing their codetherein. In addition, mobile agents are capableof dealing with non-continuous network con-nections and, consequently, they are intrinsi-cally suited for mobile computing systems.

– B-SDR Agent. The B-SDR Agent, proposedin [25,140], is a multi-agent system for rep-resenting and handling e-commerce activities.In such a system, an agent is present in eache-commerce site, handling the informationstored therein. Furthermore, an agent is asso-ciated with each customer, handling her/hisprofile. The information associated with bothsites and customer profiles is represented andhandled using a particular conceptual modelcalled B-SDR network. This latter allows touniformly manage heterogeneous data sourcesand to handle behavioural information, (i.e.,information about the behaviour of a cus-tomer on accessing data sources).

For each customer, a profile is constructedand maintained, storing information aboutthe visits she/he carries out at the vari-ous e-commerce sites. Whenever a customeraccesses an e-commerce site, the associatedagent updates her/his profile. This operationis carried out by deriving similarities betweenconcepts present in different e-commerce sites;each group of similar concepts is representedby a unique concept in the profile.When a customer accesses an e-commercesite, the site agent sends its B-SDR networkto the customer agent. This latter activates afunction for computing semantic similaritiesbetween portions of the B-SDR network as-sociated with the site and portions of the B-SDR network representing the customer pro-file. Each of these similarities represents anissue of interest for the customer which ispresent in the e-commerce site. For each in-teresting product thus selected, the site andthe customer agents cooperate for presentingto the customer a B-SDR network illustrat-ing the offers of the site for that issue. Thisprovides the capability to support the cus-tomer in searching, in the e-commerce sites,those products best satisfying her/his inter-ests. Since the computation of semantic sim-ilarities between the e-commerce site and thecustomer profile is carried out at the customerside, no information about the customer pro-file is sent to the e-commerce site, thus pre-serving customer privacy.

– MORPHEUS. MORPHEUS [178] is a com-parison shopping agent that automaticallycollects product descriptions from a group ofon-line stores on user’s behalf. Since the storesare heterogeneous, a wrapper must be builtand maintained for each store. In particular,MORPHEUS comprises: (a) a wrapper gen-erator, that is a learning module that con-structs a wrapper for each store; (b) a wrap-per interpreter, that is a module that executesthe wrapper for collecting product informa-tion from the corresponding store; (c) an out-put generator, that integrates search resultsfrom several on-line stores and produces a uni-fied output.The MORPHEUS approach is based on twoimportant assumptions, namely (i) a properkeyword is provided for each test query; (ii)


each product description contains the priceattribute which plays a key role in productsearch. Authors plan to introduce XML tech-nology in their system in the future. In thiscase, the wrapper interpreter will be simplyan XML interpreter and the DTD will con-tain the ontological information about specificdomains.

Adaptation and Personalization of e-commercesites’ presentations An important issue in theB2C e-commerce concerns the presentation of sitecontents. Indeed, a static product presentation,i.e., a presentation that is the same for all cus-tomers visiting the site, does not always meetcustomer expectations. In fact, various customersmight have different preferences for multimedia el-ements, and might be interested in different infor-mation concerning the product. It is, therefore, im-portant to provide e-commerce applications withtools able to adapt the product presentation tothe specific customer currently visiting the site.Several agent-based approaches for adapting sitepresentations have been proposed in the literature)see, e.g. [160,86,4]). In order to provide an idea oftheir behaviour we describe below two of them.

TELLIM [72] focuses on a user modeling mecha-nism that monitors the behaviour of the customerduring a visit to a site and exploits collected in-formation for constructing adaptive presentations.As an example of how this system works, Figure 9shows in the center how a car, in a car factory site,is presented at the beginning of a session. Depend-ing on the user’s behaviour, the next presentationis generated. Assume that the user has chosen thetext in the first presentation and has not enlargedthe image, then the next presentation would begenerated like the image in the left. Vice versa,if the customer has ignored the textual links, buthas enlarged the image, the presentation of thenext car would be like the image in the right. Thismeans that every product information is visible tothe customer, but preferred elements are presentedin the foreground and presumably less interestingelements are presented only as links in the back-ground.

SETA. Besides supporting product brokering(see Section 4.2.2), SETA [7], identifies also cus-tomer preferences and interests and tailors productpresentation to them. In this architecture, a DialogManager, handling the interaction with the cus-tomer, invokes a Personalization Agent and speci-

fies to it the page typology to be produced as wellas the product to be described for that particularvisiting customer. The Personalization Agent ap-plies a set of customization rules and dynamicallygenerates the page to present to the customer. Allthe decisions about the layout and the content ofa page are made on the basis of the characteris-tics stored in the customer profile. Thus, differentpages may be produced when the same product ispresented to different users.

Agent-based approaches handling adaptivity to thedevice It is worth to observe that, with the evo-lution of devices for the interaction with informa-tion services, adaptivity to customer profiles is notenough, and the access to these services dependsalso on device characteristics and on the context(e.g., work, home, holidays, etc.) where the cus-tomer is currently operating.

More in particular, with the development ofinformation technologies, ubiquitous network en-vironment (UNE) has been emerged, which in-cludes ubiquitous network and ubiquitous de-vices. In such an environment, ubiquitous networksare composed of mobile network, Internet, PSTNand other wireless access network, such 802.11WLAN, BlueTooth, Infrared communication, etc.,while ubiquitous device includes general PC, PDA,handheld, and other devices that can connect withUN. The increasing amount of Internet accessingnon-PC devices, as well as the fast development ofmobile web applications produced the necessity ofdevice independence as a desirable feature in website construction. The purpose of device indepen-dence is that the web site creator can develop webresources and services to fit all kinds of devices.In this way, any kind of device can access webresources and get the most suitable presentation.The device independence principle, obtained byexploiting user agents, is analyzed in detail in [170]of W3C. B2C e-commerce is particularly interestedin this issue, since the possibility of capturing cus-tomers by means of a proper product presenta-tion is felt as essential by the e-sellers. As an ex-ample, in [41], an agent-based approach support-ing presentation adaptivity is proposed that, be-sides user preferences, behaviour and experience,considers other features such as customer location,context and emotional state as well as technicalfeatures of the exploited device. This informationis used to personalize the way the information isaccessed and presented (e.g., browsing in a large


Fig. 8. The MOMIS Approach

Fig. 9. The TELLIM System

information space, searching for some specific dataor receiving fast and well focused “hints”). Au-thors see the environment as a distributed informa-tion source in which Personal Presentation Agentsselect the information that is appropriate to theuser within the given context and organize it ina clear, coherent and complete way. Another in-teresting approach is presented in [62], in whichsome mechanisms for adapting navigation supportto device characteristics and its context of use are

designed, thereby considering that user goals andthe resulting expected navigation behavior mightbe subject to change.

The above approaches, and many other as [57,55,54], that are designed for general web applica-tions, might be fruitfully applied in E-Commercecontexts. In [57], authors present a web site modeland publication platform, called WebUnify. Thisplatform allows to adapt the published resources todifferent device, such as PC, PDA, and Handheld.


Authors propose an ontology-based approach, bybuilding a conceptualized model of the site andcreating web resources as its instances. In this ap-proach, content authoring, style designing, contentprocessing and web site distribution are separatedfrom each other. Authors also consider the screenadaptation as a sub-issue of device independenceand implements the construction approach in anexperimental system named Orion. Moreover, adevice independent web site, in which the feed-backs are adaptive for the presentation capabilitiesof variant devices, has been built as a test-case.

Other works, as [55,54], describe new approachesproviding adaptable client interfaces for web-based information systems. Developers specifyweb-based interfaces using a high-level mark-uplanguage based on the logical structure of the userinterface and, at run-time, this single interface de-scription is used to automatically provide an in-terface for multiple web devices, as well as high-light, hide or disable interface elements dependingon the current user and user task.

In [153] a selection-based approach, where theuser selects interesting concepts by a textual inter-face, is presented, that can be adapted to a num-ber of devices with heterogeneous capabilities, in-cluding mobile phones and PDAs. The simplicityof the selection tasks and the use of text broadensthe range of devices that can be targeted. Deviceadaptation can be implemented by simply map-ping the length of terms and relation lists to thedevice characteristics.

In [56], authors examine the possibilities of ap-plying of independent user interface descriptionsin E-Commerce contexts, that can be deployed onany device to reduce the development time andcost as well as to increase the maintenability andaccessibility of the supported services.

Also, it is worth pointing out that, in the contextof device adaptivity in B2C E-Commerce, anotherimportant objective is to reduce the user-perceivedlatency in web content delivery. Many techniqueshave been proposed in the literature, as that pre-sented in [113], that describes the development ofa user adaptive content delivery mechanism thatintegrates transmission time control and transmis-sion order control. Based on the user’s preferenceprofile, such a technique dynamically prioritizes in-line objects in terms of content quality and deliv-ery order, and provides user with adapted inlineobjects. A prototype system based on this tech-nique has been implemented in Java, and gives anexample of adapted content delivery.

4.2.6. Agents supporting NegotiationNegotiation is the E2-CBB stage where the

product price is determined. Negotiation is usedin various commerce fields such as stock mar-kets (e.g, NYSE and NASDAQ), fine arts auctionhouses (e.g., Sotheby’s and Christie’s), flower auc-tions (e.g., Aalsmeer, Holland) and other diversebusinesses such as car dealerships and commission-based electronic stores. There is an evident bene-fit in dynamically negotiating a price for a prod-uct instead of fixing it; in fact, a dynamic nego-tiation allows to determine the price directly intothe marketplace instead of forcing the merchant tofix it a priori. On-line negotiation is easier to im-plement than real-world one; in fact, some impedi-ments, such as the necessity for all parties of beinggeographically co-located in an auction house andthe increment of the transaction cost, that occurin physical world negotiation, disappear in the In-ternet world. Systems like OnSale [120] and eBay’sAuctionWeb [8] do not require that participantsbe geographically co-located.

As an example, in Fastparts, members buyand sell electronic parts, products and accessoriesfrom one another, directly and anonymously. Inthe ComponentConnectTM of fastparts, memberspost bids-to-buy and offers-to-sell in records thatcontain all the information about what is offeredor bid upon. Fastparts automatically matches buy-ers to sellers at an online auction. In the Auc-tion venue, prospective buyers place winner-take-all bids on items that the seller offers for sale.

The automation of negotiation can significantlyreduce necessary time, making it possible for alarge amount of transactions to be performed insmall amounts of time, and may also remove someof the human reticence to engage in negotiation[99].

In an agent-based negotiation, agents both pre-pare bids on behalf of the buyers and evaluate of-fers on behalf of the sellers they represent. This isdone according to some negotiation strategies, andby following some negotiation protocols. The nego-tiation protocol defines the rules the agents mustfollow, determining, at each time, what an agentcan say and whom it can say to. We follow the ap-proach introduced in [61] and, in the next subsec-tions, classify agent-based approaches supportingnegotiation on the basis of the protocol they use.


Auctions Auction is today the most exploited ne-gotiation protocol in e-commerce systems. In theInternet Auction List [66] there are currently listedmore than 2,500 auctions, and various guides ex-ist on the Web that list and categorize auctionsites (such as, for instance, [27] and [63]). In anagent-based auction, there are two types of agents,namely auctioneers and bidders. One or more auc-tioneers initiate the auction, and the bidders makebids, according to the rules determined by the auc-tion protocol. At the end of the auction, a winneris determined among the bidders (for the most fun-damental concepts about auction see [85], and fora complete taxonomy of auctions see [176]), how-ever it is possible to classify the existing methodsinto two main categories, namely the single-sidedauctions and the double-sided auctions. In a single-sided auction, either bids are solicited from buyersby a monopolist seller for items to be sold, or offersare solicited from sellers by a monopsonist1 buyerfor items to be bought. In contrast, double-sidedauctions are auctions in which several buyers andsellers submit bids and offers simultaneously in thesame marketplace. The interested reader can findin [89] a complete description of the main auctionmethods belonging to those two categories.

Bilateral Negotiation Differently from an auc-tion, that is a form of one-to-many, many-to-manyor many-to-one negotiation, bilateral negotiationis one-to-one. Here, a supplier and a customersearch a mutually acceptable agreement over theterms and conditions of a trade, involving severalcontract attributes such as price, quality, deliverydate and so on. In the literature there exists a largenumber of agent-based approaches, tackling bilat-eral negotiation, that propose different strategiesfor performing a good negotiation. [61] classifiesthese approaches into three groups, namely:

– Decision making by explicitly reasoning aboutthe opponent’s behaviour. Agents belonging tothis group observe and reason about the be-haviours of their opponents, in order to de-cide what behaviour they have to assume as aresponse. Examples of such an approach are:

∗ Vidal-Durfee. [169] presents an approachfor determining when an agent should be-have strategically (i.e. by considering the

1The term ”monopsony,” first used in a print by JoanRobinson (1969), means a single buyer in a market.

behaviour of the other agents), and whenit should behave as a simple price-takerbuying at the lowest possible price. Au-thors propose a framework for developingagents with incremental modeling/learningcapabilities and show how, in some circum-stances, agents benefit by building and us-ing models of the other agents in the com-munity.

∗ Bazaar. In [181], a sequential decision mak-ing model for agent negotiation, calledBazaar, is proposed. Bazaar models agentbeliefs about the negotiation environmentby means of a probabilistic framework thatuses Bayesian learning. More specifically, abayesian network is exploited to update theknowledge each agent has about the otheragents and the environment, and bayesianprobabilities are used for leading the nego-tiation among agents.

– Decision making by finding the current bestsolution. Agents belonging to this group tryto find the negotiation solution that maxi-mizes their profits, taking into account exist-ing constraints, the opponent’s behaviour andthe current negotiation situations. Some pro-posals following such an approach are:

∗ Luo and al. In [100], authors propose afuzzy constraint based model to supporta bilateral, multi-issue negotiation. In thismodel, agents try both to conclude a gooddeal for the two involved parties and tomaximize their own profit, showing a semi-competitive behaviour merging both com-petitive and cooperative attitudes. The keyidea of such an approach is that, if an agentproposes an offer and the opponent agentdoes not accept it, the proponent agent hasto find an alternative offer that it consid-ers equally acceptable, but that is also moreacceptable to the opponent agent. If such atrade-off solution is not available, the pro-ponent agent would make a concession. Themodel is based on the exploitation of prior-itized fuzzy constraints that both representthe trade-offs between the different possi-ble values of the negotiation issues and indi-cates how to make concessions when this isnecessary. The proposed model ensures thatthe two agents reach a trade-off solution ifit exists.


∗ FeNAs. Fuzzy e-Negotiation Agents (Fe-NAs) [88] handles a multi-issue negotiationin presence of limited common knowledgeand imprecise constraints and preferences.It aims at finding a negotiation solutionthat maximizes agent’s utility subject to itsacceptability to other agents; in order tocarry out such a task, it exploits principlesof utility theory and fuzzy constraint-basedreasoning.

∗ Matos and al.. In [106], a service-orientednegotiation model is presented. A service-oriented negotiation is a real-world businessprocess management system in which theagents need to agree on who should performa particular service and under what termsand conditions. In the proposed framework,autonomous agents adapt their strategiesand tactics to the evolving circumstancesby using an evolutionary approach in whichstrategies and tactics correspond to the ge-netic material in a genetic algorithm.

∗ Kasbah. In the Kasbah system [76], astraightforward mechanism of bilateral ne-gotiation between buying and selling agentsis provided. After buying and selling agentshave been matched, the only valid actionin the negotiation protocol is for buyingagents to offer a bid to sellers. Sellingagents respond with either a binding yesor no. With this protocol, Kasbah pro-vides a buyer with three possible strategies,namely anxious, cool-headed and frugal,corresponding to an exponential, quadraticand linear function, resp., for increasingbids for a product over time. Such a negoti-ation mechanism is very intuitive to under-stand for the involved parts, and this hasdetermined a general user acceptance of it,as observed in some Media Lab experiments[32,76].

∗ Tete-a-Tete. Differently from most otheron-line negotiation systems, which behavein a competitive way, Tete-a-Tete [166,58]operates a cooperative negotiation amongthe agents. Such a negotiation ranges acrossmultiple terms of a transaction such as war-ranties, delivery times, service contracts,gift services, loan options, return policiesand so on. Tete-a-Tete does not use simpleraise or decay functions; on the contrary,

similarly to [127], it exploits an argumen-tative style of negotiation with sale agents.The evaluation constraints determined dur-ing the product brokering and merchantbrokering stages are exploited as dimen-sions of a multi-attribute utility which al-lows a customer agent to order merchantoffers w.r.t. their capability to satisfy cus-tomer preferences. It is worth pointing outthat, in this way, in Tete-a-Tete productbrokering, merchant brokering and negoti-ation phases are strictly integrated.

∗ V-Market. In [135] the authors proposean approach to the design of agent medi-ated e-commerce systems, through the def-inition and implementation of an object-oriented framework, mainly dealing withe-commerce applications based on virtualmarketplaces. A virtual marketplace is anInternet based system in which softwareagents interact and negotiate on behalf oftheir respective users, to buy, sell or findspecific goods and services. In this type ofsystem, all users can be potential buyers,sellers or both, depending on their specificinterests. The main goal of V-Market is tofacilitate the creation of this type of appli-cations, as well as to make them more ro-bust and flexible.V-Market allows developers to customizevirtual marketplaces, and define transac-tion categories on demand, incorporatingmany possible products and services thatcan be traded online. Users can create newtransaction types and items based on in-dividual needs; they can define customizedsoftware agents handling the new offeredproducts and services. Software agents inV-Market pro-actively broker and negoti-ate with interested buyers and sellers rep-resented by their own agents. They canbe customized with any set of desired be-haviors, thereby enabling the consumer tohave a virtual presence in the marketplacelooking for interesting goods, relieving cus-tomers from the need of constantly moni-toring the market. The philosophy under-lying V-Market is similar to that inspiringKasbah [32,76] in that the system concen-trates mainly on the ability to create appli-cations allowing buying and selling agentsto interact and negotiate.


∗ Faratin and al.. In [46] a formal bilat-eral negotiation model is presented. In thismodel offers and counter-offers are gener-ated by linear combinations of simple func-tions, called tactics. It is possible to varythe importance of a given modeled criterionby using different weights in the linear com-bination. Authors use the term strategy todenote the way in which agents change thetactics over time. The model defines a num-ber of tactics which agents can employ dur-ing the negotiation process and indicateshow an agent can adopt various strategiesby changing these tactics over time.

∗ MAUT. [9] proposes a generic negotiationarchitecture that uses the Multi-AttributeUtility Theory (MAUT) principle to reachagreements that satisfy multiple inter de-pendent objectives. In particular, the archi-tecture aims at both giving a constraint op-timization formulation to the MAUT prin-ciple and using a constraint optimizationsolver to find the best deals. Retrieveddeals are, then, proposed to other agentsvia a second component supporting conver-sational interactions. The system providesalso a systematic constraint relaxation pro-tocol allowing an agent, when receiving pro-posals that are disjoint from what it cancurrently accept, to generate the most ac-ceptable deals. This approach is built ontop of a Negotiation Engine, a generic ar-chitecture for coordination and negotiation.The system is currently used to automatenegotiation in the electronic componentsdomain.

∗ Paurobally and Cunningham. In [128], theauthors propose to represent a shoppingscenario between a retailer and a customerby using an adaptation of dynamic logic.Agents are able to determine where nego-tiation processes arise and the negotiationmodel which they comply with.Authors provide an abstract theory of thestates involved in the shopping scenarioand derive the paths leading to a particu-lar shopping state. Moreover, they give sev-eral negotiation models that can be followedby agents engaged in a negotiation process;these are exploited by a particular dynamiclogic for deriving the possible negotiation

paths. From an initial state, an agent canfollow a set of execution paths to performthe negotiation process and end in a statethat satisfies its goal. Stored paths form theplans of the agent. The individual goals andintentions of the agent will determine thechosen paths.When a provider and a customer negotiatethere is already some common knowledgethat the others have about each other. Thiscommon knowledge of the other’s intentionsalso plays a role in the choice of the optimalpath. As the negotiation progresses, eachagent can build up more beliefs about theother agents. With its beliefs and goals, anagent can use its logical analysis of paths toseek the best way to satisfy its goal.The advantage of exploiting a dynamic logicconsists in providing a precise language fordescribing and reasoning about actions andstates; this allows to handle various impor-tant tasks e.g. inconsistency checks. The ap-proach is also significant in that it takes intoaccount other interesting problems, such asconcurrency.Lin. In [97], authors present a researchmodel for influencing factors of pricing de-cision for an organizational procurement.The model, representing the evolution of or-ganizational market, is built based on lit-erature review of inter-organizational ne-gotiation, interviews with domestic indus-try, and empirical data analysis.. The re-lationship between framing, personal riskattitudes, performance of decision choiceand intuitive preference of pricing decisionfor an organizational procurement are pro-posed to assist the trades between indus-tries.

– Argumentation. Agents belonging to this grouptry to exchange additional information whenthey interact in a negotiation process. In sucha context, for example, an agent a that de-cides to reject the offer of another agent b, canprovide b with a motivation of its decision, ex-plaining in detail why the proposed offer can-not be accepted (e.g., the delivery time is toolong). Examples of such an approach are:

∗ Sierra and al. In [155] authors provide aformal framework in which agents can op-


erate persuasive negotiation for modifyingbeliefs and preferences of each others, us-ing an expressive communication language.Authors define the static aspects of the ne-gotiation process, as shared ontology, so-cial relations, communication language andprotocol. Furthermore, they propose a ne-gotiation model able to recognize differenttypes of arguments that agents can supplyto support their proposals and to indicatehow these arguments can be generated andinterpreted by agents.

∗ Giacomin. In [52] the author proposes amulti-agent system where agents performargumentation activity based on both knowl-edge stated in their knowledge-bases andacquired from other agents. The approachis based on the observation that in a ne-gotiation process, different arguments maycontradict with each other; this impliesthat an important problem is the compu-tation of the defeat status, namely, deter-mining which arguments emerge undefeatedfrom the conflict and which conclusionsshould be believed. The paper presents adistributed approach to argumentation andproposes two self-stabilizing algorithms forthe computation of the defeat status.

4.2.7. Other Interesting Issues: ProductConfiguring Support and Diversity-BasedRecommendation Strategies

Product Configuring Support In the last twodecades we have seen, on the one hand, an over-whelming growth of the demand for customer-individual, configurable products and, on the otherhand, a constantly improvement of the requiredsupporting software systems. Consequently, a lotof tools based on different AI techniques (e.g.,Constraint Satisfaction [73]), are today availableon the market, that can be used to avoid the re-duction of the order lead time and of the num-ber of faulty configurations. However, more andmore complex products, assembled from config-urable sub-products supplied by highly-specializedproviders, are nowadays offered to the customeras integrated solutions, thus the necessity of newtechnologies able of adequately distributing andconfiguring heterogeneous integrated systems haveto be provided. The main problem for the cus-tomers of these integrated systems is that the in-creasing complexity of products makes the inter-

action with configuration systems lengthy, due tothe fact that many configurable parameters haveto be specified during the configuration process.From this the necessity arises to create configura-tion procedures usable not only by technical peo-ple, but also by less experienced customers: con-sequently, these systems should be extended withuser-adaptive interfaces, which tailor the interac-tion to the individual user, guiding her through theconfiguration process in a personalized way. Dif-ferent recent product configuration support tech-nologies cover the above issue [134,159]. As an ex-ample, a European project called CAWICOMS [6]aims at enhancing the state of the art in con-figuration systems in order to take such issuesinto account. Within this project, an environmentcalled CAWICOMS Workbench has been realized,that supports the creation of user-adaptive Web-based configuration systems. CAWICOMS Work-bench offers both a configuration shell supportingdistributed configuration and personalization anda set of tools for the specification of the domain-dependent knowledge about the products/servicesto be configured and the types of end-users tobe served. The shell is based on advanced dis-tributed configuration and adaptive hypermediatechniques for the management of personalizedconfiguration interactions involving the coopera-tion between suppliers.

It is worth to point out that, in the CBB modelwe have proposed, when there is no single productwhich matches the needs, it is possible that sucha product can be assembled in the sense describedabove. In such a case, the model should be revisedfor taking into account also a product configuringstep.

Diversity-Based Recommendation Strategies Rec-ommender systems can be seen as a potential so-lution to the information overload problem thatare particularly well-suited for B2C e-commerceapplications. In fact, they help users to navigatethrough complex product spaces in pursuit of suit-able products by presenting sets of alternative rec-ommendations (during a series of recommenda-tion cycles) and taking advantage of user feedbackto guide future cycles. As we have seen, recom-mender systems are used both in product- andmerchant-brokering stages and, in these contexts,they usually adopt a similarity-based recommen-dation strategy, selecting cases for recommenda-tion on the basis of maximal similarity to the cur-


rent user query. However, recently this emphasison similarity has been questioned. In particular,it has been shown [158] that a set of recommen-dations may be similar to the current query butif they are also very similar to each other, i.e. ifthey lack diversity, then these recommendationsprovide only limited coverage of the recommen-dation space. Consequently, many techniques forimproving the diversity of a set of recommenda-tions without affecting similarity to the user queryhave been proposed [158,121,38]. In particular, in[121], an approach is presented that tries to im-prove the quality of recommender systems by val-idating these findings in the context of more com-plex models of collaborative filtering, as well as bydemonstrating that such techniques also preserverecommendation diversity, one of the key issues af-fecting traditional recommender systems.

Another aspect in using diversity is the pos-sibility to combine it with critiquing. Critiquingis a well-known form of user feedback in case-based recommender systems. A critique encodesthe users preference in relation to a particular fea-ture (e.g., in a digital camera recommender a usermay be allowed to indicate whether they are inter-ested in cameras with a lower resolution than theone currently presented; so ’lower resolution’ is anexample of a critique over the resolution feature).Recent research [107] demonstrates how the dy-namic generation critiques that operate over mul-tiple features can deliver significant performanceimprovements in recommendation systems.

5. The Role of Semantic Web Services in B2CApplications

The main problem deriving from the overwhelm-ing amount of information contained in the Webtoday, is that such information is not directly avail-able for automatic computation, as it would be de-sirable in many application contexts and, in par-ticular, in agent-based e-commerce applications.As noticed in [162], two main issues are recentlyemerged relative to the possibility of realizing amore powerful exploitation of Web contents: Onthe one hand, the Semantic Web can provide thetools for the explicit markup of the content ofWeb pages; on the other hand, the development ofWeb Services gives to the Web users the possibilityto exploit programs acting as independent agents

that become the producers and consumers of infor-mation and enable automation of business trans-actions. In such a context, many research has beenrecently developed for bridging the gap betweenthe present state of the Web and the Semantic Webproviding Web services. This researches proposethe vision of Web services as autonomous goal-directed agents which select other agents to coop-erate with, acting at times in client server mode,or at other times in peer to peer mode. Further-more, these Web services exploit agent ontologiesto automate both tasks and agent interactions. Inother words, Semantic Web services use the Se-mantic Web to both realize an effective informa-tion discovery capability and provide a fruitful in-teroperability. In order to achieve these objectives,it has been necessary to develop suitable formallanguages for representing and reasoning with coreconcepts of Web services. The main languages inthis context are the following:OML (Ontology Markup Language) [119] is an on-tology specification language based on ConceptualGraphs [37]. It allows the representation of con-cepts organized in taxonomies, relations and ax-ioms in first order logic.CKML (Conceptual Knowledge Markup Language)[33] is an extension of OML, by the addition oftheories, theory morphisms (which model concreteconceptual scales), and infomorphisms (which modelrealized conceptual scales). While OML defines thetypes for nouns and verbs, the theories in CKMLoffer a specification form for the modifiers, ad-jectives and adverbs. Moreover, the theories inCKML offer a means for the specification of ”con-trolled vocabularies.” Finally, CKML introducesthe distinction between a theory (abstract concep-tual scale) and a theory interpretation (concreteconceptual scale) that corresponds to the distinc-tion between a terminological ontology and an ax-iomatic ontology.DAML+OIL [39] provides modelling primitivescommonly found in frame-based languages (suchas an asserted subsumption hierarchy and the de-scription or definition of classes).OWL (Ontology Web Language) [122] has beenconceived for defining and instantiating Web on-tologies. An OWL ontology may include descrip-tions of classes, properties and their instances, andthe OWL semantics specifies how to derive logicalconsequences from a given ontology.Semantic Web Rule Language (SWRL) [161] is a


combination of two OWL sub-languages with theUnary/Binary Datalog RuleML sub-languages ofthe Rule Markup Language. This approach ex-tends the set of OWL axioms to include Horn-like rules, that model causal implications. Thus,in SWRL it is possible to combine Horn-like ruleswith an OWL knowledge base.

In the last five years, many research on E-Commerce has dealt with Web Services and theSemantic Web (see, for instance, the Workshopon Business Agents and the Semantic Web -BASeWEB ’05 - and the 2005 IEEE InternationalConference on e-Technology, e-Commerce and e-Service). In particular, in [123], authors describehow to use the language DAML-S to control the in-teraction between Web services. DAML-S definesa DAML+OIL ontology for the description of Webservices which provides: (i) the description of thecapabilities of the Web services to specify whatservice is provided; (ii) the specification of howthe Web service accomplishes its task to specifyin details how the service achieve its goals, andwhat are the requirements of potential requestersthat wish to interact with it; (iii) the specifica-tion of how the information exchanged by the Webservice and requesters maps into actual messagesexchanged by the different parties. The approachuses DAML-S to describe capabilities of Web ser-vices so that they are able of finding each otheron the basis of the information that they pro-vide, rather than referring to incidental propertiessuch as their name, port, or a free text descrip-tion of what they do; moreover, in the paper isalso showed how DAML-S can be exploited to con-trol the autonomous interaction between Web ser-vices without any need of pre-programming nei-ther the sequence of messages to exchange northe information to be transmitted. The work [95]shows how Semantic and Web Services technolo-gies can be used to support service advertisementand discovery in e-commerce. In particular, theauthors describe the design and implementationof a service matchmaking prototype which uses aDAML-S based ontology and a Description Logicreasoner to compare ontology based service de-scriptions. This approach also shows how ontolo-gies can be used to describe services so that agents(both human and automated) can advertise anddiscover services according to a semantic specifi-cation of functionality. The work [36] deals withthe possibility of representing the complex data

structures needed to support electronic commerceapplications in the semantic web using ontologies.The conventional subtype-oriented refinement ofthe ontology is supplemented by a method of coor-dinated refinement based on category theory. Au-thors show how the combined methods make on-tologies a much more powerful tool for organizingthe semantic web.

6. Conclusion: Future perspectives and newchallenges

Due to the increasing difficulty for human usersto manually manage the overwhelming amount ofinformation present in e-commerce sites today, theexploitation of software agents for automating thevarious phases involved in e-commerce processesseems to be particularly well suited. We have mod-elled the B2C e-commerce stages by using the E2-CBB model, that extends the E-CBB model byincluding the site visiting stage. Furthermore, wehave examined a large number of agent-based ap-proaches for supporting B2C e-commerce activi-ties that have been proposed in the literature inthe last decade, and we have provided a taxonomyof them w.r.t. E2-CBB.

In our opinion, three are the crucial issues forthe future development of agent technology in B2Ce-commerce arise:

– The site visiting site, that we have intro-duced in this work, is becoming more andmore important with the increasing need of e-commerce service personalization. Such a taskcan be performed in a non intrusive way byexploiting techniques able to derive the pro-file of a customer by monitoring her/his ac-tivities on the Web. The development of thesetechniques is ongoing, and several current re-search already produced solutions to problemsinvolved with this issue.

– New needs about personalization are also aris-ing, such as that of adapting the content of thee-commerce sites to the device (WAP, palm-tops, etc.) used by customers. This issue leadsto face some technological problems mainlystudied in the User Modeling domain in thepast.


– Last, but not the least, the overwhelmingproduction of both logic-based and expertsystems-based approaches for supporting thenegotiation stage in a more and more com-pletely automated way, points out the greatinterests to realize agent-systems showing“human-like” capabilities, such as predictionand autonomy, for handling such a stage.

We believe that the aforementioned issues willbe the key subjects for future research in this area.

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