摘要许多学者将因果关系这一概念应用于基于模型的诊断领域。然而，他们的研究只局限于简单因果理论。文中提出的扩展的因果理论包容了更多的信息。文中指出：扩展的因果理论的诊断空间小于等于相应简单因果理论的诊断空间。文中还将扩展的因果理论用于测试领域，证明了：对于封闭的扩展的因果理论，溯因鉴别诊断等于基于一致性鉴别诊断。这一结果可应用于测试选择的策略。

最近，许多学者意识到了将因果关系这一概念应用于基于模型的诊断领域的重要性。然而，他们的研究只局限于简单因果理论。本文提出的广义因果理论包容了更多的信息。本文指出：广义因果理论的诊断空间小于等于相应简单因果理论的诊断空间。文中给出了当待诊断系统的模型为广义因果理论时的基于模型的诊断、基于模型的中心诊断等概念，论证了基于模型的中心诊断与本原蕴含/蕴含式的直接关系，从而将本文的理论结果与实现联系起来。本文进一步指出，对广义因果理论的基于一致性中心诊断和中心溯因诊断的刻划仅仅是本文所给出的刻划的两个特殊情形.

Variable elimination algorithm was proposed for inference using Bayesian networks. In this paper, we explore further on simplifying structures of Bayesian networks to reduce computational complexity. We propose the concepts of omissible node and replaceable node, and prove that we could delete the omissible nodes and replace replaceable nodes and their ancestors without affecting inference results using Bayesian networks. In many cases, the network can be simplified by our proposed methods, and therefore, the computational efficiency could be improved in average.

The methods for computing the kernel consistency-based diagnoses and the kernel abductive diagnoses are only suited for the situation when part of the fault behavioral modes of the components are known. The characterization of the kernel model-based diagnosis based on the general causal theory is proposed, which can breakthrough the limitation of the above methods when all behavioral modes of each component are known. Using this method, when observation subsets deduced logically are respectively assigned to be empty or the whole observation set, the kernel consistency-based diagnoses and the kernel abductive diagnoses can deal with all situations. The direct relationship between this diagnostic procedure and the prime implicants/implicates is proved, which links theoretical result with implementation.

Model-based diagnosis is a new intelligent diagnosis technique which can overcome the shortcomings of traditional diagnostic methods. Hierarchical diagnosis is an important method for reducing the complexity of model-based diagnosis. In this paper, hierarchical description of the device to be diagnosed is proposed and the relationships between different abstract levels are pointed out. The soundness of hierarchical diagnosis is also proved: a diagnosis at the abstract level has a corresponding diagnosis at the detailed level. Furthermore, the incompleteness of hierarchical diagnosis is pointed out: a diagnosis at the detailed level may not have a corresponding diagnosis at its abstract level.