A scalable comparison-shopping agent for the World-Wide Web

Abstract

The Web is less agent-friendly than we might hope. Most information on the Web is presented in loosely structured natural language text with no agent-readable semantics. HTML annotations structure the display of Web pages, but provide virtually no insight into their content. Thus, the designers of intelligent Web agents need to address the following questions: (1) To what extent can an agent understand information published at Web sites? (2) Is the agent's understanding sufficient to provide genuinely useful assistance to users? (3) Is site-specific hand-coding necessary, or can the agent automatically extract information from unfamiliar Web sites? (4) What aspects of the Web facilitate this competence? In this paper we investigate these issues with a case study using the ShopBot. ShopBot is a fullyimplemented, domain-independent comparison-shopping agent. Given the home pages of several on-line stores, ShopBot autonomously learns how to shop at those vendors. After its learning is com...