A hybrid algorithm for finding minimal unsatisfiable subsets in over-constrained CSPs

Abstract

Minimal Unsatisfiable Subsets (MUSes) are the subsets of constraints of an overconstrained constraint satisfaction problem (CSP) that cannot be satisfied simultaneously and therefore are responsible for the conflict in the CSP. In this paper, we present a hybrid algorithm for finding MUSes in overconstrained CSPs. The hybrid algorithm combines the direct and the indirect approaches to finding MUSes in overconstrained CSPs. Experimentation with random CSPs reveals that the hybrid approach is not only quite efficient but when operating under a time bound it finds a more representative set of MUSes. C 2011 Wiley Periodicals, Inc.

Deformation Analysis of an Ultra-High Arch Dam under Different Water Level Conditions Based on Optimized Dynamic Panel Clustering

“…After the weight coefficient of the comprehensive distance of dam deformation is calculated by the critical method, the number of zonings of dam deformation needs to be determined. The CSP index is used to evaluate the best value of the single link condensed hierarchical clustering algorithm [40,41] (panel data clustering is a single link condensed hierarchical clustering algorithm). The implementation steps are as follows:…”

“…According to the clustering results of dam deformation zoning, the monitoring points PL11-4, PL11-5, PL13-4, PL13-5, PL16-4, and PL16-5 in the deformation sensitive area in the middle of the dam are selected as key monitoring points. The key monitoring parts of dam deformation are modeled and analyzed, in which the water pressure component is taken as the fourth power, and the temperature component is replaced by two groups of harmonic factors [40], so as to obtain the monitoring model of representative measuring points. The fitting value, predicted value, and the residual value of deformation at monitoring points are shown in Figure 12.…”

A Max-Term Counting Based Knowledge Inconsistency Checking Strategy and Inconsistency Measure Calculation of Fuzzy Knowledge Based Systems

“…As finding minimal inconsistent subsets or maximal consistent subsets is NP-complete, the most efficient algorithm is not known yet, and there are a number of heuristic optimizations that can be used to substantially reduce the size of the search space. In practice, heuristic information [8,9], optimization [10,11], and hybrid techniques [12,13] are recognized to reduce time complexity. In the latest research, McAreavey et al presented a computational approach to finding and measuring inconsistency in arbitrary knowledge bases [14], while Mu et al gave a method for measuring the significance of inconsistency in the viewpoints framework [15].…”