CoreDiag: Eliminating Redundancy in Constraint Sets

TL;DR

CoreDiag is a new algorithm designed to identify minimal non-redundant constraint sets, reducing effort in knowledge base development and maintenance, especially in distributed environments, demonstrated through empirical studies.

Contribution

The paper introduces CoreDiag, an algorithm for efficiently finding minimal cores in constraint sets, improving knowledge base management in distributed systems.

Findings

CoreDiag effectively reduces redundant constraints in knowledge bases.

Empirical results show improved efficiency in configuration systems.

Redundancy elimination simplifies knowledge base maintenance.

Abstract

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.