A Resource-Bounded Interpretation-Centric Approach to Information Gathering

Victor Lesser, Bryan Horling, Frank Klassner, Anita Raja, Thomas Wagner, Shelley Zhang

The vast amount of information available today on the World Wide Web (WWW) has great potential to improve the quality of decisions and the productivity of consumers. However, the WWW’s large number of information sources and their different levels of accessibility, reliability and associated costs present human decision makers with a complex information gathering planning problem that is too difficult to solve without high-level filtering of information. In many cases, manual browsing through even a limited portion of the relevant information obtainable through advancing information retrieval (IR) and information extraction (IE) technologies (Larkey and Croft 1996; Lehnert and Sundheim 1991) is no longer effective. The time/quality/cost tradeoffs offered by the collection of information sources and the dynamic nature of the environment lead us to conclude that the user cannot (and should not) serve as the detailed controller of the information gathering (IG) process. Our solution to this problem is to integrate different AI technologies, namely scheduling, planning, text processing, and interpretation problem solving, into a single information gathering agent, BIG (resource- Bounded Information Gathering), that can take the role of the human information gatherer.


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