Case-based reasoning (CBR) is a well-established problem solving paradigm
that has been used in a wide range of real-world applications. Despite
its great practical success, work on the theoretical foundations of CBR is
still under way, and a coherent and universally applicable methodological
framework is yet missing. The absence of such a framework inspired the
motivation for the work developed in this thesis. Drawing on recent research
on preference handling in Artificial Intelligence and related fields, the goal of
this work is to develop a well theoretically-founded framework on the basis
of formal concepts and methods for knowledge representation and reasoning
with preferences.