Model-based Diagnosis (MBD) with multiple observations is a currently complicated problem with many applications and solving this problem is attracting more and more attention. This paper propose an improved algorithm, called Improved implicit Hitting Set Dualization (IHSD), which is the integration of gate domination in recent works for computing cardinality-minimal aggregated diagnoses in MBD problems. First, our approach works by separating components into dominated components and non-dominated components according to structure of diagnosis system. The separated components are modelled as hard clauses and soft clauses separately. Additionally, two feasible approaches, called IHSDa and IHSDb, are proposed to expand one cardinality-minimal aggregated diagnosis to more diagnoses. Experimental results on 74XXX and ISCAS85 benchmarks clearly show that IHSD algorithm improves HSD, DC and DC*. Moreover, IHSDa and IHSDb outperform HSD on solving more diagnoses.

具有多个观测值的基于模型的诊断（MBD）是当前具有许多应用的复杂问题，并且解决该问题正引起越来越多的关注。本文提出了一种改进的算法，称为改进的隐式命中集二元化（IHSD），它是最近工作中门支配的集成，用于计算 MBD 问题中的基数最小聚合诊断。首先，我们的方法根据诊断系统的结构将组件分为主导组件和非主导组件。分离的组件分别建模为硬子句和软子句。此外，提出了两种可行的方法，称为 IHSDa 和 IHSDb，将一种基数最小聚合诊断扩展到更多诊断。在 74XXX 和 ISCAS85 基准上的实验结果清楚地表明，IHSD 算法改进了 HSD、DC 和 DC*。此外，IHSDa 和 IHSDb 在解决更多诊断方面优于 HSD。