  Learning and adaptation is a fundamental property of intelligent agents. In
the context of adaptive information filtering, a filtering agent's beliefs
about a user's information needs have to be revised regularly with reference to
the user's most current information preferences. This learning and adaptation
process is essential for maintaining the agent's filtering performance. The AGM
belief revision paradigm provides a rigorous foundation for modelling rational
and minimal changes to an agent's beliefs. In particular, the maxi-adjustment
method, which follows the AGM rationale of belief change, offers a sound and
robust computational mechanism to develop adaptive agents so that learning
autonomy of these agents can be enhanced. This paper describes how the
maxi-adjustment method is applied to develop the learning components of
adaptive information filtering agents, and discusses possible difficulties of
applying such a framework to these agents.
