共查询到19条相似文献,搜索用时 343 毫秒
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不精确信息进行表达和推理逐渐成为一种必然的需求,而经典的本体并不适合处理这种不确定的信息.设计了一种支持模糊粗糙本体的推理机,其最大的特点是处理的对象是模糊粗糙本体,从而解决了不确定信息的推理问题.对模糊粗糙本体模型进行了研究,着重介绍了该模糊粗糙本体推理机的总体结构、功能及推理机中各模块的作用,通过对模糊粗糙本体进行约简和去粗糙处理,最终将对模糊粗糙本体的推理转化为经典的推理. 相似文献
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Deep Web信息量大,主题专一,信息质量好.然而Deep Web信息存在着不确定问题,因此难以对其进行知识表示和推理.基于动态模糊逻辑理论,提出了一种新的描述逻辑,即动态模糊描述逻辑(DFDLs).给出了DFDLs形式化定义以及DFDLs的语法和语义,设计了DFDLs的tableau的推理算法和策略.采用动态模糊描述逻辑对面向Deep Web的不确定知识进行表示并实现合理的推理和利用,能更好地表达Deep Web的动态和模糊信息. 相似文献
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中医舌诊系统中智能推理可信度的研究 总被引:2,自引:0,他引:2
中医舌诊是一个极其复杂的过程,是以舌症彩色图像的识别为主,并综合考虑脉象和其它临床数据的中医医疗诊断方法.可信度方法是人工智能领域的一种不确定推理方法,它通过量化不确定知识将不确定信息确定化,被广泛用于处理各种随机不确定信息.通过融合模糊逻辑推理、神经网络技术及可拓学方法,研究了一种基于竞争神经网络的中医舌诊智能推理模型,以VC 6.0和SQL Server2000为开发工具,实现了对中医专家诊断推理过程的模拟.并在推理过程中嵌入了可信度的评价体系,对输入模式信息和专家经验信息的关系引入了可信度度量,借鉴贝叶斯理论方法对神经网络的推理结果的可信度进行了研究,从而使诊断准确率大大提高,且系统使用简洁、方便. 相似文献
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1引言 贝叶斯网络是有向无环图.它是一种概率推理技术,能从不完全、不精确或不确定的知识和信息中作出推理.主观贝叶斯网络引入了主观贝叶斯方法,成功地解决了在实际应用中的诸多困难. 相似文献
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为解决探地雷达的目标识别问题,提出了一种基于雷达扫描数据、实地探测情况、历史信息和已有水文地质信息,并利用D-S证据理论这一具有解决多数据源不确定信息推理和融合特点的理论对目标进行综合识别的方法.实现了探地雷达目标在不确定条件下获得较高可信度的识别.试验结果验证了该理论在探地雷达目标识别上的有效性和可行性. 相似文献
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Merging uncertain information with semantic heterogeneity in XML 总被引:1,自引:1,他引:0
Semistructured information can be merged in a logic-based framework [6, 7]. This framework has been extended to deal with
uncertainty, in the form of probability values, degrees of beliefs, or necessity measures, associated with leaves (i.e. textentries)
in the XML documents [3]. In this paper we further extend this approach to modelling and merging uncertain information that
is defined at different levels of granularity of XML textentries, and to modelling and reasoning with XML documents that contain
semantically heterogeneous uncertain information on more complex elements in XML subtrees. We present the formal definitions
for modelling, propagating and merging semantically heterogeneous uncertain information and explain how they can be handled
using logic-based fusion techniques.
Anthony Hunter received a B.Sc. (1984) from the University of Bristol and an M.Sc. (1987) and Ph.D. (1992) from Imperial College, London.
He is currently a reader in the Department of Computer Science at University College London. His main research interests are:
Knowledge representation and reasoning, Analysing inconsistency, Argumentation, Default reasoning and Knowledge Fusion.
Weiru Liu is a senior lecturer at the School of Computer Science, Queen's University Belfast. She received her B.Sc. and M.Sc. degrees
in Computer Science from Jilin University, P.R China, and her Ph.D. degree in Artificial Intelligence from the University
of Edinburgh. Her main research interests include reasoning under uncertainty, knowledge representation and reasoning, uncertain
knowledge and information fusion, and knowledge discovery in databases. She has published over 50 journal and conference papers
in these areas. 相似文献
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基于精确本体的推理机不能够直接应用到粗糙本体的知识推理中,因此目前还没有适合粗糙本体的推理机。根据粗糙本体的特点,将其中的粗糙集、粗糙描述逻辑、粗糙包含和知识推理作为研究对象,在此基础上将基于描述逻辑的推理方法与基于规则的推理方法相结合,提出一种粗糙本体支持的知识推理框架,实现了粗糙本体的推理功能,解决了针对不确定信息的知识推理问题。 相似文献
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A fuzzy Petri net-based expert system and its application to damageassessment of bridges 总被引:1,自引:0,他引:1
Lee J. Liu K.F.R. Weiling Chiang 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》1999,29(3):350-370
In this paper, a fuzzy Petri net approach to modeling fuzzy rule-based reasoning is proposed to bring together the possibilistic entailment and the fuzzy reasoning to handle uncertain and imprecise information. The three key components in our fuzzy rule-based reasoning-fuzzy propositions, truth-qualified fuzzy rules, and truth-qualified fuzzy facts-can be formulated as fuzzy places, uncertain transitions, and uncertain fuzzy tokens, respectively. Four types of uncertain transitions-inference, aggregation, duplication, and aggregation-duplication transitions-are introduced to fulfil the mechanism of fuzzy rule-based reasoning. A framework of integrated expert systems based on our fuzzy Petri net, called fuzzy Petri net-based expert system (FPNES), is implemented in Java. Major features of FPNES include knowledge representation through the use of hierarchical fuzzy Petri nets, a reasoning mechanism based on fuzzy Petri nets, and transformation of modularized fuzzy rule bases into hierarchical fuzzy Petri nets. An application to the damage assessment of the Da-Shi bridge in Taiwan is used as an illustrative example of FPNES. 相似文献
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故障诊断经常受到多种不确定性和模糊性因素的影响,针对不确定性的故障诊断问题,利用直觉模糊集较好的表达不确定性信息的优势和Petri网较好的并行处理以及图形处理问题的能力,构建了直觉模糊Petri网模型。由于将直觉模糊推理转化为矩阵运算的过程中有非隶属度参数的参与,因此推理结果可提供更多的信息。根据实际故障诊断中的模糊推理问题,给出了带有权值、阈值等参数条件下新的直觉模糊推理算法。通过获取和处理故障诊断中的不确定性和模糊性的知识,该算法将故障诊断过程转化为利用直觉模糊Petri网的直觉模糊推理过程。实际燃气轮机故障诊断模型案例表明了所给直觉模糊推理算法的有效性。 相似文献
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From the early developments of machines for reasoning and decision making in higher-level information fusion, there was a need for a systematic and reliable evaluation of their performance. Performance evaluation is important for comparison and assessment of alternative solutions to real-world problems. In this paper we focus on one aspect of performance assessment for reasoning under uncertainty: the accuracy of the resulting belief (prediction or estimate). We propose a framework for assessment based on the assumption that the system under investigation is uncertain only due to stochastic variability (randomness), which is partially known. In this context we formulate a distance measure between the “ground truth” and the output of an automated system for reasoning in the framework of one of the non-additive uncertainty formalisms (such as imprecise probability theory, belief function theory or possibility theory). The proposed assessment framework is demonstrated with a simple numerical example. 相似文献
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D-S证据理论在决策支持系统中的应用 总被引:2,自引:1,他引:1
D-S证据理论提供了一种解决多数据源不确定信息推理和融合的有效方法。证据理论能够对各自独立的证据加以综合给出一致性结果,并能处理具有模糊和不确定信息的合成问题,最终达到信息互补。与其他推理方法相比更符合人类思维决策过程。为此,提出一种基于D-S证据理论的灾害决策支持方法,并根据试验结果验证了该方法的有效性和可行性。 相似文献
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It has been recognized that past experiences of a decision maker often plays a pivotal role in solving new problem instances. Therefore, the ability to model human reasoning processes has become an important subject of research in recent years. In many applications, the reasoning process must deal with uncertainty inherent in the problem domain. This research addresses the issue of supporting the model formulation and data acquisition processes for situations that (i) operate under uncertain conditions, and (ii) utilize evidential information that is gathered in stages. A theoretical framework is presented for the probabilistic formulation of the reasoning process that incorporates past experiences. The model is validated by testing its performance on simulated data, and is shown to work well when a sufficiently large number of cases are available for estimating probabilities. The probabilistic reasoning system can revise beliefs in an intuitively appealing and theoretically sound manner when information is acquired in an incremental fashion. Two dynamic information gathering strategies are discussed for such a reasoning system, one using information theoretic techniques, and the other using decision theoretic techniques. 相似文献
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Pervasive computing creates possibilities for presenting highly personalised information about the people, places and things
in a building. One of the challenges for such personalisation is the creation of the system that can support ontological reasoning for several key tasks: reasoning about location; personalisation of information about location at the right level of detail;
and personalisation to match each person’s conceptions of the building based on their own use of it and their relationship
to other people in the building. From pragmatic perspectives, it should be inexpensive to create the ontology for each new
building. It is also critical that users should be able to understand and control pervasive applications. We created the PERSONAF
(personalised pervasive scrutable ontological framework) to address these challenges. PERSONAF is a new abstract framework
for pervasive ontological reasoning. We report its evaluation at three levels. First, we assessed the power of the ontology
for reasoning about noisy and uncertain location information, showing that PERSONAF can improve location modelling. Notably,
the best ontological reasoner varies across users. Second, we demonstrate the use of the PERSONAF framework in Adaptive Locator,
an application built upon it, using our low cost mechanisms for non-generic layers of the ontology. Finally, we report a user
study, which evaluated the PERSONAF approach as seen by users in the Adaptive Locator. We assessed both the personalisation
performance and the understandability of explanations of the system reasoning. Together, these three evaluations show that
the PERSONAF approach supports building of low cost ontologies, that can achieve flexible ontological reasoning about smart
buildings and the people in them, and that this can be used to build applications which give personalised information that
can provide understandable explanations of the reasoning underlying the personalisation. 相似文献