A Logic-Based Approach for Adaptive Information Filtering Agents

Adaptive information filtering 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 can be improved. Applying a logic-based framework for representation, learning, and matching in adaptive information filtering agents is promising since users' changing information needs can automatically be deduced by the agents. In addition, the inferred changes can be explained and justified based on formal deduction. This paper examines how the AGM belief revision logic can be applied to the learning processes of these agents.

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