CoreDiag: Eliminating Redundancy in Constraint Sets
This addresses the time-consuming and error-prone maintenance of constraint-based systems, particularly in distributed knowledge engineering scenarios, but appears incremental as it focuses on optimizing existing constraint sets rather than introducing a new paradigm.
The paper tackles the problem of redundant constraints in knowledge bases for configuration, recommender, and scheduling systems, which increase computational effort and maintenance costs, by presenting CoreDiag, a new algorithm for determining minimal non-redundant constraint sets, with empirical validation on commercial configuration knowledge bases.
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.