Belief revision for adaptive information filtering agents

Agent-based information filtering alleviates the problem of information overload on the Internet by proactively scanning through the incoming stream of information on behalf of the users. Nevertheless, users' information needs will change over time. Therefore, it is essential for the information filtering agents to learn and adapt to the users' changing information needs in order to maintain the accuracy of the filtering process. Applying logic-based representation and adaptation to adaptive information filtering agents is promising since the semantic relationships among information items can be captured and reasoned about during the agents' learning and adaptation processes. This opens the door to a more responsive reinforcement learning than can be obtained from a purely statistical approach. The AGM belief revision paradigm that models rational and minimal change of an agent's beliefs offers a sound theoretical foundation for constructing the learning components of adaptive information filtering agents. This paper describes a symbolic framework for representing domain knowledge in an adaptive information filtering agent, and illustrates how the AGM belief revision paradigm can be applied to develop the agent's learning mechanism.