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题名:
反绎推理和诊断问题求解的研究
作者: 徐越
答辩日期: 1994
专业: 计算机软件
授予单位: 中国科学院软件研究所
授予地点: 中国科学院软件研究所
学位: 博士
摘要: 反绎推理是人类逻辑推理的一种基本方式,它所具有的多解性和高或然性以及由此引起的高计算复杂性是当前AI研究所遇到的困难之一。另一方面,反绎问题普遍存在于人类的日常生活和生产活动中,所以,反绎推理系统有着十分广阔的应用前景。可见,对反绎推理的研究是一项很有意义的工作。本文以反绎推理为核心,从几个侧面探讨了一系列有关的问题。首先,本文在知识表示上提出了一种表现力较强的扩充因果网络。这种因果网络能够描述复杂的非独立性反绎问题。基于这种知识表示,本文提出了一个真值维护方法CTM。并对CTM进行了形式化研究,讨论了CTM的语义。在CTM的基础上,本文建立了一个反绎推理模型,并对推理结果的或然性给出了一个启发式的计算方法。该方法通过将求解集中于本文定义的强预测性解释范围内来缩小搜索空间,从而改善求解效率。在此之后,本文还进一步探讨了非单调推理问题,对上述模型进行了推广和改进,使其能处理一定的非单调反绎问题。在从一般性角度探讨了反绎推理的知识表示和求解方法之后,本文还对作为一类反绎问题的诊断推理从基于模型的角度进行了深入的研究。对Raiman & de Kleer提出的临界推理法进行了改进和扩充,提出了弱临界推理法。该方法放宽了对临界诊断空间的限制条件,从而使临界推理更具一般性。另外,本文还将临界推理与基于行为模式的诊断方法相结合,探讨了故障行为的检测问题。基于模型的诊断涉及对实际设备的模型化。本文对深层模型的表示也进行了研究,提出了一种基于框架结构的模型化方法。框架结构良好的模块化和嵌套结构为实现分层诊断策略和深层模型的自动生成提供了条件。本文基于这种模型化方法,设计了一个分层诊断推理模型,并在一定条件下探讨了深层模型的自动生成问题。本文对联接机制的知识表示能力和神经网络的并行计算能力,将一个高层次的知识处理过程转化为一个神经网络的计算过程,从而改善诊断问题求解的计算复杂性。最后,本文还设计实现了一个以上述部分研究结果为基础的实验性的数字电路的故障诊断系统,介绍了系统的结构、功能和工作流程,并对实验结果进行了分析和说明。
英文摘要: Abduction, characterized as finding the most plausible composite hypothesis that explains all observation data, is a basic form of logical inference. Abductive reasoning is widely used in many field, such as plans, diagnoses natural language understanding, image reconstruction and machine learning. The main features of abductive reasoning include multi-solutions, high probability and high computational complexity which make it intractable generally. Approach to abduction is very significant. Several relative problems about abductive reasoning and diagnostic reasoning were discussed in the dissertation. The main research work in the dissertation includes the following points. (1). At first, we proposed a kind of augmented causal network which is used to represent abductive knowledge The augmented causal network has high descriptive power which can represent incompatility abduction problems and cancellation abduction problems. Based on the causal networks, a truth maintenance method CTM is proposed. The CTM can accept any acyclic clause set not restricted to Horn clauses. So it can deal with more general abduction problems. The semantics of CTM is also discussed in this paper. (2). A abductive inference model based on CTM and a heuristic method to calculate the probability of inference results are proposed. The model uses a strategy that focuses the inference on finding the strong predictive expanations defined in this paper to limit the search space. And the strategy can improve the inference efficiency. After this, we discussed The nonmonotonic abduction and put forward a default abductive inference model, which can deal with certain nonmonotonic abduction problems. (3). Diagnoses are typical abduction problems. In the dissertation, we investigated diagnostic problem solving from the aspect of model-based diagnosis We improved and augmented the Critical Reasoning Method proposed by Raiman & de Kleer. The improved method called Weak Critical Reasoning relaxes the limitation to critical diagnoses. So the method is more general than the one proposed by Rainman & de Kleer. (4). For model-based diagnosis, a crucial task is to build a model for the device to be diagnosed. In this paper, A modelling method based on frameworks was presented By means of the advantages of frameworks, we proposed a method to hierarchically model domain problems and a method to generate device models from component models automatically. (5). We put forward a connectionist diagnostic model, which transforms the symbolic inference of diagnoses into a process of numerical calculation so that the computational complexity of diagnostic reasoning can be alleviated. (6). We developed a prototype model-based diagnosis system for digital circuit fault diagnosis, which uses the methods and strategies discussed in the dissertation.
语种: 中文
内容类型: 学位论文
URI标识: http://ir.iscas.ac.cn/handle/311060/7078
Appears in Collections:中科院软件所

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Recommended Citation:
徐越. 反绎推理和诊断问题求解的研究[D]. 中国科学院软件研究所. 中国科学院软件研究所. 1994-01-01.
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