Role Of Intelligent Agents And Intelligent

Published Date: 02 Nov 2017

Ques:

What are the roles of intelligent agents and intelligent interfaces in e-Commerce?

Ans:

Intelligent agents represent a new breed of software with significant potential for a wide range of Internet applications. They have been successfully used for personal assistants, intelligent user interfaces, and managing electronic mail. Recently, agents have been applied to electronic commerce, promising a revolution in the way we conduct business, whether business-to-business, business-to-customer or customer-to-customer. This article gives a brief introduction to intelligent agents and electronic commerce, followed by a review of agent technologies involved in buying and selling. Typology, taxonomy, and classification of agents are presented. Several agent-mediated electronic commerce systems are analyzed in the context of a general model of the buying process. Several lists of related Internet links should help readers to gather additional relevant information.

Introduction

In recent years the Internet (World Wide Web) due to its exponential growth enabled substantial progress in new information society functions such as online commerce. Latest studies of online spending habits of consumers by Forrester have shown that the growth has been explosive , increasing from $2.4 billion in 1997 to $8.0 billion in 1998 and $20.2 billion in 1999 and still growing at a rapid pace. Electronic commerce entails business-to business, business-to-customer and customer-to-customer transactions. It encompasses a wide range of issues including security, trust, reputation, law, payment mechanisms, advertising, ontologies, electronic product catalogs, intermediaries, multimedia shopping experiences, and back office management. Agent technologies can be applied to any of these areas still; the potential of the Internet for truly transforming commerce is largely unrealized to date. Electronic purchases remain mostly non-automated. While information about different products and vendors is easily accessible and orders and payments can be dealt with electronically, a human is still in the loop in all stages of the buying process. Traditional shopping activities require a large effort from a human buyer collecting and interpreting information on merchants, products and services, making an optimal purchase decisions and finally entering appropriate purchase and payment information. Software agents help automate a variety of activities, mostly time consuming ones, and thus lower the transaction costs. Software agents differ from "traditional" software in that they are personalized, social, continuously running and semi-autonomous. In this way, e-commerce is becoming more user-friendly, semi-intelligent and human-like.

Intelligent agents are a member of the bot family—software programs that operate unattended, usually on the Internet. Therefore, agents are sometimes referred to as bots. Individuals or organizations use intelligent agents to perform functions or tasks that otherwise would involve human interaction or repetition. Operating independently on behalf of their users, some intelligent agents mimic human behavior and thought processes and are able to make decisions, learn, and interact with other intelligent agents. Intelligent agents come in stationary and mobile varieties, meaning that they can either reside on individual computer systems or travel from server to server across the Internet to carry out different tasks.

According to Online, there is agreement among many authors in the field of artificial intelligence that a true intelligent agent must be social, adaptable, proactive and autonomous. In the early 2000s, intelligent agent technology was still evolving and a single agent with all four of these traits had not been created. Multi-agent systems, or groups of intelligent agents in which each exhibits one or several of the four behaviors, were in development. Nevertheless, many evolving forms of intelligent agents existed in everything from search engines to computer help systems.

In the world of e-commerce, intelligent agents known as shopping bots are used by consumers to search for product and pricing information on the Web. Each shopping bot operates differently, depending on the business model used by its operator. In one scenario, shopping bots direct users to retailers who, by subscribing for a fee, are part of a closed system.

Shopping.Yahoo and Shop@AOL are examples of this model. Open systems are a more common arrangement and involve agents that include the entire Web in their searches.

Shopping bots have become very popular with consumers. In Time, International Data Corp. revealed that about 4 million shoppers took advantage of the technology in October 2000 alone. However, they weren't popular with some companies because of their ability to initiate bidding wars and eat away profits in the process.

In addition to searching for durable goods, electronics and other items, consumers also were expected to use intelligent agents more frequently in the area of personal finance. In Bank Systems & Technology, a report from Andersen Consulting stated that personal financial bots (PFBs) would reshape this industry by becoming "virtual financial intermediaries" that carry out transactions and searches for financial products via ATMs, wireless phones, and televisions. While this concept had not been widely adopted in the early 2000s, it posed a possible threat to the umbrella model used by many traditional banks, in which several products and services—including loans, credit cards and insurance—were offered to customers by one provider.

Intelligent agents also provide varying levels of customer service on the Web. In addition to providing direct answers to common questions, they can save companies money by helping customers narrow down their problem before speaking to a live customer service rep. One emerging intelligent agent was able to anticipate what customers might want based on the Web pages they looked at. Created by Denver-based Finali, the netSage also was able to mimic human emotions, including disappointment if it was unable to answer a customer's question. In addition to reducing customer service costs, intelligent agents are useful for converting potential customers, many of whom abandon online "shopping carts" without making purchases, to actual customers.

Although they have been used more frequently in the consumer arena, intelligent agents also have potential applications in the area of business-to-business e-commerce. For example, a manufacturer requiring many different parts to create one product could use an intelligent agent to not only find the best prices from different suppliers, but to consider many other variables that impact the total manufacturing cost, such as shipping, cost and quality. Theoretically, the intelligent agent could do this more quickly and efficiently than a human. Ultimately, this could result in significant cost savings for companies.

By maximizing efficiency and convenience, intelligent agents will likely play increasingly important roles in the world of e-commerce. In Computer Reseller News, the Gartner Group estimated that bots would account for as much as four percent of all IT spending by 2002. The following statement from IBM, printed in Computer world, is further indication of the key role this technology will play in the near future: "We envision the Internet some years hence as a seething milieu in which billions of economically motivated software agents find and process information… Agents will naturally evolve from facilitators into decision-makers."

As IBM's vision becomes reality, security will become a concern for buyers and sellers alike. When consumers send agents out with strategic objectives and the ability to negotiate terms and conditions and make purchases on their behalf, they will need assurances that the agents can't be manipulated or compromised by other agents. Likewise, companies will need to watch for agents that are used for malicious purposes.

Intelligent Agents

There are many definitions of what the term "agent" denotes based on different approaches, expectations and visions. As pointed out by Bradshaw, one person’s "intelligent agent" is another’s person "smart object". Shoham describes a software agent as a software entity which functions continuously and autonomously in a particular environment often inhabited by other agents and processes. The requirement for continuity and autonomy derives from human desire that an agent be able to perform activities in a flexible and intelligent manner responsive to changes in the environment without constant human supervision. An agent that functions over a long period of time should be able to adopt from its experience. Further, we expect an agent to inhabit an environment with other agents and processes, to be able to communicate and cooperate with them, and perhaps move from one place to another Consistent with the requirements of a particular problem, each agent might possess to a greater or lesser degree the following attributes:

· Reactivity: the ability to selectively sense and act.

· Autonomy: goal-directedness, proactive and self-starting behavior.

· Collaborative behavior: can work in collaboration with other agent to achieve a common goal.

· "Knowledge-level" communication ability: the ability to communicate with human and other agents with language more resembling human-like speech than symbol-level protocols.

· Inferential capability: can act on abstract task specification using prior knowledge of general goals and preferred methods to achieve flexibility.

· Temporal continuity: persistence of identity and state over long periods of time.

· Personality: the capability of manifesting the attributes of a believable character such as emotion.

· Adaptively: being able to learn and improve with experience.

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 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: Persona Logic, 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 prior. 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.

Intelligent interfaces

An intelligent interface cannot just respond passively to its user’s instructions and queries. Rather, it must be able to take the initiative in its dialogue with the user. For example, consider a hypothetical intelligent interface that provides help in using the UNIX1 operating system. A user would be able to ask such a system in English (or some other natural-language) for advice about how to do things, for definitions of terminology, or for advice in solving problems. The consultant program would then provide solutions in English like a real human consultant would. Also like a human consultant, such a program may need to take the initiative during the consultation. For instance, consider the following user interaction with the hypothetical consultation program:

Why do we need intelligent interfaces?

* Interfaces are getting too complex

* Interfaces are too inflexible

* Interfaces don't change when our needs change

* Interfaces don't work with each other

Scope of intelligent interfaces

Typically, we require of an intelligent interface that it should employ some kind of intelligent technique. What, exactly, counts as an intelligent technique will vary over time, but the following list is a fairly complete list of the kinds of techniques that today are being employed in intelligent interfaces:

User Adaptivity: Techniques that allow the user - system interaction to be adapted to different users and different usage situations.

User Modelling: Techniques that allow a system to maintain knowledge about a user.

Natural Language Technology: Techniques that allow a system to interpret or generate natural language utterances, in text or in speech,

Dialogue Modelling: Techniques that allow a system to maintain a natural language dialogue with a user, possible in combination with other interaction means (multimodal dialogue),

Explanation Generation: Techniques that allow a system to explain its results to a user.

But providing such a list of technologies does not capture the essential feature of the intelligent interface research area: an intelligent interface must utilise technology to make an improvement: the resulting interface should be better than any other solution, not just different and technically more advanced.