Intelligent Agents for Web-based Tasks: An Advice-Taking Approach

Published 1 January 1998

Jude Shavlik, Tina Eliassi‐Rad

Citations25

TL;DR

The architecture provides an appealing middle ground between nonadaptive agent programming languages and systems that solely learn user preferences from the user’s ratings of pages, and how advice is mapped into neural network implementations of the two functions.

Abstract

We present and evaluate an implemented system with which to rapidly and easily build intelligent software agents for Web-based tasks. Our design is centered around two basic functions: ScoreThisLink and ScoreThisPage. If given highly accurate such functions, standard heuristic search would lead to efficient retrieval of useful information. Our approach allows users to tailor our system&apos;s behavior by providing approximate advice about the above functions. This advice is mapped into neural network implementations of the two functions. Subsequent reinforcements from the Web (e.g., dead links) and any ratings of retrieved pages that the user wishes to provide are, respectively, used to refine the link- and pagescoring functions. Hence, our architecture provides an appealing middle ground between nonadaptive agent programming languages and systems that solely learn user preferences from the user&apos;s ratings of pages. We describe our internal representation of Web pages, the major predicates ...

Keywords

Computer ScienceDecision Sciences

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