Dynamic Refinement of Feature Weights Using Quantitative Introspective Learning

Abstract

Recently more and more researchers have been supporting the view that learning is a goaldriven process. One of the key properties of a goal-driven learner is introspectiveness - the ability to notice the gaps in its knowledge and to reason about the information required to fill in those gaps. In this paper, we introduce a quantitative introspective learning paradigm into case-based reasoning (CBR) . The result is an integrated problem-solving model which will learn introspectively feature weights in a case base in order to be responsive dynamically to its users. In contrast to the existing qualitative methods for introspective learning, our model has the advantage of being able to capture accurate learning information in the interactions with its users. A CBR system equipped with quantitative introspective learning ability can allow the feature weights to be captured automatically and to track its users' changing preferences continuously. In such a system, while the reasoning part is still case-based, the learning part is shouldered by a quantitative introspective learning model. Weight learning and evolution are accomplished in the background . The effectiveness of this integration will be demonstrated through a series of empirical experiments.