Research and Advances
Artificial Intelligence and Machine Learning

Agents in E-Commerce

Let systems of Web-based competitive self-interested agents do the negotiating, buying, and selling.
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  1. Introduction
  2. Medium of Actions
  3. Author

One big lesson about Web-based e-commerce is already clear: Success comes to those who find ways to deliver to consumers orders of magnitude better ways to shop. Amazon.com aggregated book catalogs, delivering on its first day of operation in 1995 instant access to more than a million titles; few physical bookstores stock more than 100,000 titles. Online brokerages, such as E*Trade and eSchwab, have found ways to give full-service securities trading at 10% of the traditional $200 commission. In fact, the current online trading price leader Brown & Co. provides online trades for $5 each, selling itself through the clever slogan "Five bucks? That’s not a commission, it’s a tip." So how can we in the software industry achieve order-of-magnitude gains in user costs, customer price, improved service, and increased variety, when all of the obvious applications might seem to be claimed already?

Forrester Research, an information consulting firm in Cambridge, Mass., estimates that Internet-generated electronic transactions will grow exponentially from a combined worth of about $14 billion in 1997 to $327 billion within five years. Moreover, a study by the World Bank found that 80% of current profitability from products and services on the Internet simply did not exist 12 to 18 months ago. What this means, to those who prefer to see the glass as half full, is that there is still at least $313 billion of opportunity out there—most associated with Internet products and services that have not yet been invented and are likely to be based on technologies that today are only emerging. However, this opportunity is available only through the dislocation of existing business processes by new more cost-effective, scalable, Net-intrinsic business models. We can expect the emergence of dramatically new kinds of supply chains, distribution channels, and dynamic markets that use new kinds of intelligent distributed computational processes—we call agents.

This special section is based on a straightforward vision of Internet evolution. The Web, in order to avoid being overwhelmed by its own informational baggage, has to grow from a dumb publishing model toward a more refined and intelligent one. This evolution will be based on all sorts of new and open technologies, like distributed objects, the Java programming language, semantic tagging, and the extensible markup language (XML). However, a bit more murky is how agents will fit into the future of the Web.


There is still at least $313 billion of opportunity out there—most associated with Internet products and services that have not yet been invented.


I have a kind of love-hate relationship with the term "agent." Too many mediocre, me-too software products are being marketed by resourceful product managers as so-called agent technology. It’s not a search engine, it’s a search agent. But even with this baggage, the idea of agents remains alluring. When it comes to agents, even though the Internet industry has generally underdelivered, it still has not overpromised—an important distinction.

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Medium of Actions

I offer a definition of agents we can all probably agree on. When asked what an agent is, I usually say that just as a word processor works through the medium of words, and spreadsheets work through numbers, agents work through the medium of actions. For example, an agent might remind or automatically prompt me to email Joe, find me that article on IBM’s new chip, or buy Yahoo stock when it drops to 80. In a more technical vein, agents are atomic software entities operating through autonomous actions on behalf of the user—machines and humans—without constant human intervention.

This definition allows us to branch into all sorts of agent types: intelligent, mobile, multiagent systems, and profiling. It also makes it possible for this section to explore them by way of several features and a number of related sidebars. Maes et al. of the MIT Media Laboratory provide a fundamental survey of e-commerce agent technology, revealing how agents can be used to manage the process of buying and selling on the Web. Sandholm of Washington University explores automated negotiation that optimizes contract responsibilities and benefits for all contracting parties. Lange and Oshima of General Magic outline seven key reasons for developing mobile agents.

Wong et al. of Mitsubishi Electric and AT&T Research Laboratories discuss the true meaning of agent mobility, thanks to Java. Koblick of Mitsubishi Electric details the Java mobile agent technology known as Concordia. Tai and Kosaka of IBM’s Tokyo Research Laboratory describe how IBM’s Aglets project launches mobile agents into the Web.

Hayes-Roth et al. of Extempo Systems and Stanford University introduce new interactive-agent-character user interfaces and how to put them to work doing business with human customers.

Glushko et al. of Veo Systems discuss how XML can make the Web an agreeable environment for interacting agents and e-commerce. And Smith and Poulter of ontology.org and Computer Sciences Corp. explore why shared ontology is so important in XML-based trading architectures.

These technologies will all have an increasingly profound influence on the process of e-market formation, because they intrinsically represent systems of competitive self-interested agents—which is what commerce is all about. We all hope you find this section as exciting to read as it was to research, write, and edit.

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