Belief revision and possibilistic logic for adaptive information filtering agents
Refereed conference paper presented and published in conference proceedings

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AbstractPrototypes of adaptive information agents have been developed to alleviate the problem of information overload on the Internet. However, the explanatory power and the learning autonomy of these agents are weak. A logic based framework for the development of information agents is appealing since semantic relationships among information objects can be captured and reasoned about. This sheds light on better explanatory power and higher learning autonomy of information agents. The paper illustrates how the AGM belief revision and possibilistic logic can be applied to develop the learning and the filtering components of adaptive information filtering agents. Their impact on the agents' learning autonomy and explanatory power is also discussed.
All Author(s) ListLau R., Ter Hofstede A.H.M., Bruza P.D., Wong K.F.
Name of Conference12th IEEE Internationals Conference on Tools with Artificial Intelligence, ICTAI 2000
Start Date of Conference13/11/2000
End Date of Conference15/11/2000
Place of ConferenceVancouver
Country/Region of ConferenceCanada
Volume Number2000-January
Pages19 - 26
LanguagesEnglish-United Kingdom
KeywordsAdaptive filters, Art, Australia, Computational Intelligence Society, Feedback, Information filtering, Information filters, Information retrieval, Internet, Logic

Last updated on 2021-18-10 at 23:29