Belief revision and possibilistic logic for adaptive information filtering agents

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. This paper illustrates how the A GM 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.