Abstract

We present a system capable of modeling human newspaper readers. It is based on the extraction of reported speech, which is subsequently converted into a fuzzy theory-based representation of single statements. A domain analysis then assigns statements to topics. A number of fuzzy set operators, including fuzzy belief revision, are applied to model different belief strategies. At the end, our system holds certain beliefs while rejecting others.

Reference

Ralf Krestel, René Witte, and Sabine Bergler. Creating a Fuzzy Believer to Model Human Newspaper Readers. In: Advances in Artificial Intelligence, Proceedings of the 20th Conference of the Canadian Society for Computational Studies of Intelligence (Canadian AI 2007), May 28-30, 2007, Montréal, Québec, Canada. Springer LNAI 4509, pp.476-488.

Bibtex entry (also for download):

@InProceedings{KrWiBe_CAI2007,
  author = 	 {Ralf Krestel and Ren{\'{e}} Witte and Sabine Bergler},
  title =        {{Creating a Fuzzy Believer to Model Human Newspaper Readers}},
  booktitle =	 {Proc.\ of the 20th Canadian Conference on 
                  Artificial Intelligence (Canadian A.I. 2007)},
  pages =	 {489--501},
  year =	 {2007},
  editor =	 {Z. Kobti and D. Wu},
  series =	 {LNAI 4509},
  address =	 {Montr{\'{e}}al, Qu{\'{e}}bec, Canada},
  month =	 {May 28--30},
  publisher =	 {Springer},
}

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Canadian AI 2007 had an acceptance rate of 17.7%.

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