A multi-agent system that facilitates scientific publications search

ABSTRACT

It is very difficult for beginners to define and find the most relevant literature in a research field. They can search on the web or look at the most important journals and conference proceedings, but it would be much better to receive suggestions directly from experts of the field. Unfortunately, this is not always possible and systems like CiteSeer and GoogleScholar become extremely useful for beginners (and not only). In this paper, we present an agent-based system that facilitates scientific publications search. Users interacting with their personal agents produce a transfer of knowledge about relevant publications from experts to beginners. Each personal agent observes how publications are used and induces behavioral patterns that are used to create more effective recommendations. Feedback exchange allows agents to share their knowledge and virtual communities of cloned experts can be created to support novice users. We present a set of experimental results, obtained using CiteSeer as a source of information, that show the effectiveness of our approach.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process; Relevance feedback

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Intelligent agents; Multiagent systems
      I.2.6 Learning
          Subjects: Knowledge acquisition

General Terms:
Design, Experimentation

Keywords:
CiteSeer, implicit culture, learning agents, learning from observations, multi-agent system, personal agents, publication search