Intelligent Agents In Ecommerce

Published Date: 02 Nov 2017

commonly known as e-commerce, is a type of industry where buying and selling of product or service is conducted over electronic systems such as the Internet and other computer networks. Electronic commerce draws on such technologies as mobile commerce, electronic funds transfer, supply chain management, Internet marketing, online transaction processing, electronic data interchange (EDI), inventory management systems, and automated data collection systems. Modern electronic commerce typically uses the World Wide Web at least at one point in the transaction's life-cycle, although it may encompass a wider range of technologies such as e-mail, mobile devices social media, and telephones as well.

Electronic commerce is generally considered to be the sales aspect of e-business. It also consists of the exchange of data to facilitate the financing and payment aspects of business transactions.

E-commerce can be divided into:

E-tailing or "virtual storefronts" on Web sites with online catalogs, sometimes gathered into a "virtual mall"

The gathering and use of demographic data through Web contacts and social media

Electronic Data Interchange (EDI), the business-to-business exchange of data

E-mail and fax and their use as media for reaching prospects and established customers (for example, with newsletters)

Business-to-business buying and selling

The security of business transactions

the role of intelleligent agents and intelligent interface in e- commerce is as follows-

Intelligent Agents In Ecommerce

Artificial intelligence (AI) continues to play a significant role in many leading information systems. In the past, its use has been limited due to its complexity, monolithic designs and lack of knowledgeable system developers. AI contribution is now crucial in nondeterministic systems such as workflow, data mining, production scheduling, supply chain logistics, and most recently, ecommerce.

Its new form is not the monolithic AI systems of the past, but distributed artificial intelligence, popularly known as intelligent agent technology. Intelligent agent technology is the next logical step in overcoming some shortcomings in e-commerce. Namely, successful computer systems underlying ecommerce require judgment and the

knowledge of experts such as buyers, contract negotiators and marketing specialists .

It is useful to explore the roles of agents as mediators in electronic commerce in the context of a common framework. The presented model stems from consumer buying behavior research and comprises the

actions and decisions involved in buying and using goods and services. The model covers many areas, but focuses primarily on retail markets (although most concepts pertain to business-to-business and business-to consumers markets as well).

Also, electronic commerce covers a broad range of issues, some of which are beyond the scope of this consumer buying behavior model. There are a variety of descriptive theories and models that attempt to capture buying behavior, such as the Nicosia model, the Howard- Sheth model, the Engel-Blackwell model, the Bettman information-processing model, and the Andreasen model . These models all share a similar list of six fundamental stages of the buying process, which also elucidate where agent technologies apply to the shopping experience:

Identification: This stage characterizes the buyer becoming aware of some unmet need by stimulating through product information. Agents can play an important role for those purchases that Intelligent Agents in E-commerce are repetitive (supplies) or predictable (habits). One of the oldest and simplest examples of software agents are so called "monitors": continuously running programs which monitor a set of sensors or data streams and take action when a certain pre-specified condition apply .

There are many examples in abundant use, one very familiar is a "notification agent" called "Eyes" by Amazon.com, which monitors the catalog of books for sale and notifies the customer when certain events occur that may be of interest to the customer (e.g., when a new book in category X becomes available).

Brokering:

a) Product Brokering: once a buyer has identified a need to make a purchase (possibly with the assistance of a monitor agent), the buyer has to determine what to buy through a critical

evaluation of retrieved product information. There are several agents systems that lower consumers’ search cost when deciding which products best meet their needs: PersonaLogic, Firefly, and Tete-a-Tete . The result of this stage is a consideration set of goods.

b) Merchant Brokering: this stage combines the consideration set from the previous stage with merchant-specific alternatives to help determine who to buy from. The problem that was exposed here was that most of the merchants do not want to compete on price only, and want the value-added services (e.g., warranty, availability, delivery time, reputation) to be included in consumers’ buying decision.

Negotiation: in this stage, price and other terms of the transaction are settled on. Real-world negotiation increases transaction costs that may be too high for either consumers or merchants. There are also impediments in the real world to using negotiation such as time constraints, frustrations, all parties to be geographically co-located etc., which mostly disappear in the digital world.

The majority of business-to-business transactions involve negotiation. In retail, we are mostly familiar with fixed prices. The benefit of dynamically negotiating the price for a product instead of fixing it is that it relieves the merchant from needing to determine the value of the good a priori . Rather, this burden is pushed to the marketplace.

Payment and Delivery: this stage can either signal the termination of the negotiation stage or occur sometimes afterwards (in either order). In some cases, the available payment or delivery options can influence product and merchant brokering.

Product Service and Evaluation: this post-purchase stage involves product service, customer service, and an evaluation of the satisfaction of the overall buying experience and decision. Given the above set of stages, we can identify the roles of agents as mediators in electronic commerce. The nature of agents make them well-suited for mediating those consumer behaviors involving information filtering and retrieval, personalized evaluations, complex coordination, and time-based interactions.

Those roles correspond most notably to the need identification, product and merchant brokering, and negotiation stages of the buying behavior model. lists the six buying behavior stages and shows where several representative agent systems fall within the space.

Intelligent interface in e-commerce-

This issue is devoted to intelligent human-computer interfaces -- interfaces that raise the computer to the human level, rather than requiring that the human adapt to the computer. An interface may be intelligent because it can communicate using human language, or because it performs intelligent functions, or because it adapts to a specific task and user. In all these cases, an intelligent interface makes interaction with the computer easier, more intuitive, and more flexible.

The area of intelligent interfaces is just one facet of the broad area of human-computer interaction. Other important areas include graphical user interface design, usability, human factors, visualization, immersive environments, and intelligent tutoring systems. However, this issue focuses on intelligent interfaces, representing the spectrum of MITRE activities in this area.

As the computer's role has evolved over the past 50 years, so have our expectations of the human-computer interface. Computers were initially used for complex numerical calculations, such as cryptography or ballistics. Computers were scarce, large, and expensive. Access was limited to a small set of highly trained enginee.

By the mid-1960s, computers were also routinely used in industry, for record keeping or transaction processing systems, such as airline reservations. But computers still cost hundreds of thousands of dollars and access was restricted to specialists. No one worried about making computers usable to non-specialists, anymore than they worried about the human-computer interface for a slide rule. The computer remained an expensive tool, used by specially trained technologists.

However, with the 1980s came the "personal computer" revolution-- the price dropped, availability increased, and many more people had access to computers. This was followed in the mid-90s by another revolution -- the World Wide Web.

The Web provided ready access to the Internet and transformed computers into communications devices, connecting people with distributed information (e.g., Web-accessible document collections) and with other people, through e-mail and collaborative computing environments. Computers had become much smaller, much cheaper, much more powerful, and much more widely distributed.

Today, computers and computing devices are all around us -- on everyone's desk at work, on the wrists of supermarket employees, in the car, and on the telephone. The range of demands on human-computer interfaces is increasing: preschoolers play computer games, grade school children are trying out interactive learning, teens need the Web access to do their homework, retired people use e-mail to keep in touch with distant families, and businesses are establishing e-commerce Web sites. The human-computer interface has suddenly become a critical element in this revolution. The interface must make the computer easy to learn, easy to use, and adaptable to a wide range of users in varied environments.

This short history contains three dimensions that have had particular importance for human-computer interfaces: the use by broader, less "expert" segments of society, the integration of computing into new areas and applications; and the miniaturization of computers. All of these require adjustment in the human-computer interface.

One way to make computers accessible to a broader range of users is to make interaction with a computer more like interaction with a person. For example, computers might converse using regular human language, particularly speech. And if computers were capable of communicating via speech, gesture, or handwriting, their keyboards could become unnecessary and they could be made smaller.

These changes would allow us a move toward "information appliances," such as an "intelligent telephone" that would allow users to "talk" to the Web, as well as co-workers. Also, such a device would allow us to retrieve e-mail, receive relevant news feeds, or send a fax.

Future computers might offer the ability to create adaptive interfaces that can be quickly tailored to specific user needs. The interface may even be able to learn how to best interact with a user, just as a person learns how to best interact with someone they see everyday.

Finally, when a computer communicates via human language and adapts to user needs, users begin to think of it as an "intelligent agent" or a human surrogate, instead of thinking of it as a tool. This creates a new paradigm, where computers are semi-autonomous participants doing specific tasks for one or more people, such as finding resources, scheduling meetings, or making travel arrangements.

This issue's articles explore how intelligent interfaces can close the gap between the user and the computer. Our authors write about spoken language technology, which allows a person to converse with a computer; interface flexibility that can provide output tailored to individual users; and the routine but useful things "intelligent multimodal participants" could do in a collaborative human-human environment.

The articles illustrate the range of MITRE technical contributions, from advanced visionary prototypes, to custom solutions for sponsor needs, to infrastructure for data collection, and standards development. All of these articles describe different ways in which intelligent human-computer interfaces help to bridge the gap between human and machine -- communicating in human language, supporting human-human interaction, and providing flexible user-tailored