Ontological reasoning in the design space exploration of advanced cyber–physical systems

Cyber–physical systems are becoming increasingly complex. In these advanced systems, the different engineering domains involved in the design process become more and more intertwined. Therefore, a traditional (sequential) design process becomes inefficient in finding good design options. Instead, an integrated approach is needed where parameters in multiple different engineering domains can be chosen, evaluated, and optimized to achieve a good overall solution. However, in such an approach, the combined design space becomes vast. As such, methods are needed to mitigate this problem.In this paper, we show a method for systematically capturing and updating domain knowledge in the context of a co-design process involving different engineering domains, i.e. control and embedded. We rely on ontologies to reason about the relationships between parameters in the different domains. This allows us to derive a stepwise design space exploration workflow where this domain knowledge is used to quickly reduce the design space to a subset of likely good candidates. We illustrate our approach by applying it to the design space exploration process for an advanced electric motor control system and its deployment on embedded hardware.     

多重多维模糊推理算法的连续性和逼近性

针对多重、多维模糊推理情形,细致地研究了几类模糊推理算法是否满足连续性和逼近性,并进一步讨论了这几类算法对逼近误差的传播性能。把模糊推理算法看成是一个模糊集合到另一个模糊集合的映射,选用海明距离作为两模糊集的距离度量方法,证明了在模糊假言推理和模糊拒取式推理情形,几类多重多维模糊算法都拥有连续性。当多重多维模糊算法满足还原性时就具有逼近性;该模糊算法都不会放大逼近误差。结果对构建模糊控制系统和模糊专家系统时选用和分析模糊推理算法有一定的指导作用。     

Human-oriented and machine-oriented reasoning: Remarks on some problems in the history of Automated Theorem Proving

Examples in the history of Automated Theorem Proving are given, in order to show that even a seemingly mechanical activity, such as deductive inference drawing, involves special cultural features and tacit knowledge. Mechanisation of reasoning is thus regarded as a complex undertaking in cultural pruning of human-oriented reasoning. Sociological counterparts of this passage from human- to machine-oriented reasoning are discussed, by focusing on problems of man-machine interaction in the area of computer-assisted proof processing.     

Why does language matter to artificial intelligence?

Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of language (in reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored.     

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of knowledge they contain (i.e., the level of support they provide) and in their ability to learn (i.e., their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings.Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics (e.g., Prolog) are not, however, well suited to higher-order applications. Instead, we employ Prolog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and training instance upon which EBG relies, we extend Prolog with the necessity operator of modal logic. We develop a meta-interpreter realizing EBG for the extended language, Prolog, and provide examples of higher-order EBG.     

一个基于实例推理的软件过程知识复用方法

过程资产库的建立和基于过程资产的项目过程定义是一个组织的软件能力成熟度达到已定义级的关键标志,传统的过程资产库中存储的过程往往是通过文字描述和使用者判断其适用性,用于项目过程的建立。本文提出了一种基于实例推理(CBR)的过程知识复用方法,通过过程特征的刻画和实例相似度计算,实现过程实例的提取和复用。实践证明,该方法有助于提高过程知识复用的准确度和自动化程度,为软件过程改进中的过程建立提供有效手段。     

变电站智能告警专家系统的不确定性知识表示

在变电站智能告警专家系统中,针对业务知识的不确定性和复杂性,文中设计了一种不确定性知识的表示方法,可以使采用该方法构建的知识库更有利于推理机的推理和知识库的自学习。该方法根据领域知识将变电站信号进行分层分类,以子知识库的形式进行知识的组织,克服了知识的复杂性。而且它采用一种模糊产生式表示业务知识,将模糊推理规则映射为模糊产生式,解决了知识的不确定性问题。实验分析表明,该方法适合进行变电站关联信号的告警推理,结合系统对知识的自学习机制,可以有效降低告警的差错率和虚警率。     

中国耕地细碎化的理论解析与研究框架

基于演绎推理法和文献研究法,探索耕地细碎化的概念、过程、机制和效应,并构建耕地细碎化研究框架,为耕地细碎化研究提供理论指导。结果表明:①耕地细碎化是耕地在自然-人为双重因素驱动下,耕地地块在形态上不断分割、变小,耕地产权在主体上不断细分、多元的过程,并分别表现为相互关联又有所区别的耕地景观细碎化和耕地权属细碎化; ②耕地细碎化研究应遵循“诊断-机制-效应-治理”的理论框架,耕地细碎化诊断需从形态和权属两方面构建评价指标体系,形成机制可从自然切割力、利用切割力、设施切割力和管理切割力4个方面揭示不同因素的作用机制,效应需揭示不同尺度下耕地细碎化形态和产权的趋势性转折对经济、社会和生态的影响,治理研究应按照“问题识别治理潜力治理路径保障措施”的逻辑顺序构建。     

CBR技术在自动配色系统中的应用研究

为提高复杂多变条件下的配色准确率,提出了基于案例推理（case-based reasoning,CBR）的新型自动配色技术,并给出了原型--基于案例推理的配色系统(CBRCMS). CBRCMS对配方库组织结构、配方检索、配方复用、学习调整等关键技术进行了探讨.CBRCMS基于大量的历史优良配方,以色度迭代逼近技术作为配色精度的基本保证,并将其作为CBR配方复用的主要方法.实验数据显示,CBR的引入可明显提高配色准确率,CBR技术的自适应、自学习特点对传统配色技术是很好的补充.     

全寿命周期主动设备管理辅助决策

目前,电网设备管理大多通过被动的计划检修完成,只注重遵循相关运维规程按设备运行时间安排检修工作,忽略对设备全寿命健康状况的评估与管理。本文提出计及设备全寿命周期运行状态的多源融合主动资产管理方法。首先,根据资产时间、逻辑及历史知识提出全寿命周期三维资产管理模型。接着采用设备案例推理方法,从电网安全、稳定、经济运行角度,应用迁移学习方法对案例进行自学习与案例库强化。最后,将所提出方法应用于存在海量多源异构数据的设备全寿命周期主动管理的实际案例中。结果表明,主动资产管理可以有效自动辨识设备运维状态,保证资产管理策略的可行性、适用性及安全性,为运维人员提供有力辅助决策。