A Logic-Based Approach for Adaptive Information Filtering Agents

Adaptive information ltering agents have been developed to alleviate the problem of information overload on the Internet by learning a user's changing information needs and autonomously scanning through the incoming stream of information according to the user's latest information needs. However, the explanatory power and the learning autonomy of these agents should be improved. Applying a logic-based framework for representation, learning, and matching in adaptive information ltering agents is promising since users' changing information needs can automatically be deduced by the agents. In addition, the inferred changes can be explained and justiied based on formal deduction. This paper proposes a logic-based model for adaptive information ltering agents. Particularly, it illustrates how the AGM paradigm of belief revision can be applied to the learning processes of these agents. The impact of a logic-based framework on agents' learning autonomy and explanatory power is discussed through examples.