  Constraint-based environments such as configuration systems, recommender
systems, and scheduling systems support users in different decision making
scenarios. These environments exploit a knowledge base for determining
solutions of interest for the user. The development and maintenance of such
knowledge bases is an extremely time-consuming and error-prone task. Users
often specify constraints which do not reflect the real-world. For example,
redundant constraints are specified which often increase both, the effort for
calculating a solution and efforts related to knowledge base development and
maintenance. In this paper we present a new algorithm (CoreDiag) which can be
exploited for the determination of minimal cores (minimal non-redundant
constraint sets). The algorithm is especially useful for distributed knowledge
engineering scenarios where the degree of redundancy can become high. In order
to show the applicability of our approach, we present an empirical study
conducted with commercial configuration knowledge bases.
