Sequential pattern mining and belief revision for adaptive information retrieval

Autonomous information agents alleviate the information overload problem on the Internet. The AGM belief revision framework provides a rigorous foundation to develop adaptive information agents. The expressive power of the belief revision logic allow a user´s information preferences and contextual knowledge of a retrieval situation to be captured and reasoned about within a single logical framework. Contextual knowledge for information retrieval can be acquired via sequential pattern mining. This paper illustrates a novel approach of integrating the proposed data mining method into the belief revision based adaptive information agents to improve the agents´ learning autonomy and prediction power.