BIG: An agent for resource-bounded information gathering and decision making

Victor Lesser, Bryan Horling, Frank Klassner, Anita Raja, Thomas Wagner, Shelley XQ. Zhang
<span title="">2000</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/weoplee4x5anpi62cco5v4higa" style="color: black;">Artificial Intelligence</a> </i> &nbsp;
The World Wide Web has become an invaluable information resource but the explosion of available information has made Web search a time consuming and complex process. The large number of information sources and their different levels of accessibility, reliability and associated costs present a complex information gathering control problem. This paper describes the rationale, architecture, and implementation of a next generation information gathering system-a system that integrates several areas
more &raquo; ... f Artificial Intelligence research under a single umbrella. Our solution to the information explosion is an information gathering agent, BIG, that plans to gather information to support a decision process, reasons about the resource trade-offs of different possible gathering approaches, extracts information from both unstructured and structured documents, and uses the extracted information to refine its search and processing activities.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0004-3702(00)00005-9">doi:10.1016/s0004-3702(00)00005-9</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wwxxj2zlzfhchddvetkwry4c2y">fatcat:wwxxj2zlzfhchddvetkwry4c2y</a> </span>
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