Belief revision and possibilistic logic for adaptive information filtering agents

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Venue:
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Year:
2000

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Cited 3

Weakening conflicting information for iterated revision and knowledge integration

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Weakening conflicting information for iterated revision and knowledge integration

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1

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Abstract

Abstract: Prototypes 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.