INFORMEDQX: Informed Conflict Detection for Over-Constrained Problems

Authors

  • Viet-Man Le Graz University of Technology, Graz, Austria University of Economics, Hue University, Hue, Vietnam
  • Alexander Felfernig Graz University of Technology, Graz, Austria
  • Thi Ngoc Trang Tran Graz University of Technology, Graz, Austria School of Hospitality and Tourism, Hue University, Hue, Vietnam
  • Mathias Uta Siemens Energy AG, Germany

DOI:

https://doi.org/10.1609/aaai.v38i9.28932

Keywords:

KRR: Diagnosis and Abductive Reasoning, KRR: Applications, KRR: Knowledge Engineering, KRR: Preferences

Abstract

Conflict detection is relevant in various application scenarios, ranging from interactive decision-making to the diagnosis of faulty knowledge bases. Conflicts can be regarded as sets of constraints that cause an inconsistency. In many scenarios (e.g., constraint-based configuration), conflicts are repeatedly determined for the same or similar sets of constraints. This misses out on the valuable opportunity for leveraging knowledge reuse and related potential performance improvements, which are extremely important, specifically interactive constraint-based applications. In this paper, we show how to integrate knowledge reuse concepts into non-instructive conflict detection. We introduce the InformedQX algorithm, which is a reuse-aware variant of QuickXPlain. The results of a related performance analysis with the Linux-2.6.3.33 configuration knowledge base show significant improvements in terms of runtime performance compared to QuickXPlain.

Published

2024-03-24

How to Cite

Le, V.-M., Felfernig, A., Tran, T. N. T., & Uta, M. (2024). INFORMEDQX: Informed Conflict Detection for Over-Constrained Problems. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 10616-10623. https://doi.org/10.1609/aaai.v38i9.28932

Issue

Section

AAAI Technical Track on Knowledge Representation and Reasoning