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A System for Building Intelligent Agents that Learn to Retrieve and Extract Information

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Abstract

We present a system for rapidly and easily building instructable and self-adaptive software agents that retrieve and extract information. Our Wisconsin Adaptive Web Assistant (WAWA) constructs intelligent agents by accepting user preferences in the form of instructions. These user-provided instructions are compiled into neural networks that are responsible for the adaptive capabilities of an intelligent agent. The agent’s neural networks are modified via user-provided and system-constructed training examples. Users can create training examples by rating Web pages (or documents), but more importantly WAWA’s agents uses techniques from reinforcement learning to internally create their own examples. Users can also provide additional instruction throughout the life of an agent. Our experimental evaluations on a ‘home-page finder’ agent and a ‘seminar-announcement extractor’ agent illustrate the value of using instructable and adaptive agents for retrieving and extracting information.

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Authors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing, Box 808, L-560, Livermore, CA, 94551, USA

    Tina Eliassi-Rad

  2. Computer Sciences Department, University of Wisconsin-Madison, 1210 West Dayton Street, Madison, WI, 53706, USA

    Jude Shavlik

Authors
  1. Tina Eliassi-Rad
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  2. Jude Shavlik
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Eliassi-Rad, T., Shavlik, J. A System for Building Intelligent Agents that Learn to Retrieve and Extract Information. User Model User-Adap Inter 13, 35–88 (2003). https://doi.org/10.1023/A:1024009718142

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