软件可靠性模型应用中的不一致性与软件可靠性专家系统

关于软件系统故障行为的统计假设,是软件可靠性模型的理论基础 。由此产生应用中的不一致性,使用户对这些估测结果迷惑不解,“谁的结果最可信”？我们以“故障数据是软件可靠性分析的基础”为原则,采用人工智能技术,建立软件可靠性专家系统（SRES：Software Reliability Expert System),以解决软件可靠性模型应用中的不一致性难题。最后,报告该系统的使用及与国外一些软件可靠性量测工具的比较。     

人工智能的逆向工程——反向智能研究综述

在大数据时代,人工智能得到了蓬勃发展,尤其以机器学习、深度学习为代表的技术更是取得了突破性进展.随着人工智能在实际场景中的广泛应用,人工智能的安全和隐私问题也逐渐暴露出来,并吸引了学术界和工业界的广泛关注.以机器学习为代表,许多学者从攻击和防御的角度对模型的安全问题进行了深入的研究,并且提出了一系列的方法.然而,当前对机器学习安全的研究缺少完整的理论架构和系统架构.从训练数据逆向还原、模型结构反向推演、模型缺陷分析等角度进行了总结和分析,建立了反向智能的抽象定义及其分类体系.同时,在反向智能的基础上,将机器学习安全作为应用对其进行简要归纳.最后探讨了反向智能研究当前面临的挑战以及未来的研究方向.建立反向智能的理论体系,对于促进人工智能健康发展极具理论意义.     

人工智能与法律结合的现状及发展趋势

人工智能并没有一个统一的定义,但若一个计算机系统能做人需要智能才能做的事,一般便认为这样的计算机系统具有人工智能。因此,人工智能被广泛应用于许多需要人类智能的领域,如法律、医疗、金融、电子商务等,其中法律是当前的一个重要应用领域。因此,文中主要从立法(人工智能系统辅助立法以及立法监管人工智能系统,特别是自主驾驶汽车)、知法守法(法律信息的检索、法律文书的生成和审核)、司法(证据收集、法律推理以及在线纠纷解决)等方面综述了人工智能和法律结合的研究现状以及发展趋势,希望能引导更多人投入这个研究领域。     

人工智能技术在电气自动化控制中的应用研究

当前,人工智能技术作为新兴的智能技术,广泛应用在于电气自动化控制中。本文首先对人工智能技术进行 了简要概述,其次通过分析人工智能技术的特点和优势,着重探讨人工智能技术在电气自动化控制、操作、维修中的应用。     

大数据智能:从数据拟合最优解到博弈对抗均衡解

数据驱动的机器学习（特别是深度学习）在自然语言处理、计算机视觉分析和语音识别等领域取得了巨大进展,是人工智能研究的热点。但是传统机器学习是通过各种优化算法拟合训练数据集上的最优模型,即在模型上的平均损失最小,而在现实生活的很多问题（如商业竞拍、资源分配等）中,人工智能算法学习的目标应该是是均衡解,即在动态情况下也有较好效果。这就需要将博弈的思想应用于大数据智能。通过蒙特卡洛树搜索和强化学习等方法,可以将博弈与人工智能相结合,寻求博弈对抗模型的均衡解。从数据拟合的最优解到博弈对抗的均衡解能让大数据智能有更广阔的应用空间。     

Monitoring process control systems

Recently, there has been considerable interest in the applications of artificial intelligence to process control. To date, most work has been focused on how to enhance the process control loop itself or how to interact directly with the process and the control system. For near term applications, we claim that artificial intelligence should be used purely in a monitoring role. Rather than being involved in the active control of the system, artificial intelligence should be used to identify and assist with rapidly correcting any problems which cause the process control system to fail.We also claim that it is too early to be concerned with real time issues. Rather, the emphasis should be on on-line systems, with particular emphasis placed on aids in knowledge base development and trending capabilities to provide early prediction of failure.We have developed a system called Annie that embodies these principles into a working process monitoring system.     

系统仿真的新技术探讨－（3）：人工智能

系统仿真技术近几十年得到了很快地发展，已广泛运用于许多领域。现有的系统对数值模型的仿真研究得较深入。但对认知模型和感知模型的仿真（模拟人的思维、问题求解、外界感知的能力），现有的系统对此研究得较少。本文探讨系统仿真的新技术之一：人工智能，主要介绍专家系统、人工神经网、模糊系统在系统仿真中的应用。     

学习的机制在软件开发中的应用

人类的知识来源于学习,伴随着人工智能的发展,学习的机制也被广泛地应用于计算机科学的各个领域中.在软件开发中,提高软件适应性是追求的目标之一.本文阐述了如何通过在软件开发中应用学习的机制,包括不依赖于人工智能原理的方法和基于人工智能原理的方法,来提高软件适应性.两个研究实例展示了如何运用这一思想来开发出具备自我学习能力的软件.     

基于EEG的脑机接口发展综述

随着无线传输、机器学习、人工智能等技术的进步,基于脑电图(EEG)的脑机接口(BCI)技术的研究相应增加,作为一种变革性的通讯和控制技术,脑机接口可以广泛地应用于康复医疗、游戏娱乐、军事应用、家居智能等领域,具备千亿级别的应用市场;综述了基于EEG的典型脑机接口范式,包括MI-BCI、P300-BCI、SSVEP-BCI等范式的基本原理、研究现状和典型应用场景,对各类范式的优缺点进行了评价,提出了当前研究中面临的技术和伦理等方面的风险挑战,并对其发展和应用前景作了展望。     

A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence

Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision-making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision-making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%.     

面向人工智能和大数据的高效能计算

【目的】本文主要分析人工智能和大数据应用随着迅速增大的数据规模,给计算机系统带来的主要挑战,并针对计算机系统的发展趋势给出了一些面向人工智能和大数据亟待解决的高效能计算的若干研究方向。【文献范围】本文广泛查阅国内外在超级计算和高性能计算平台进行大数据和人工智能计算的最新研究成果及解决的挑战性问题。【方法】大数据既为人工智能提供了日益丰富的训练数据集合,但也给计算机系统的算力提出了更高的要求。近年来我国超级计算机处于世界的前列,为大数据和人工智能的大规模应用提供了强有力的计算平台支撑。【结果】而目前以超级计算机为代表的高性能计算平台大多采用CPU+加速器构成的异构并行计算系统,其数量众多的计算核心能够为人工智能和大数据应用提供强大的计算能力。【局限性】由于体系结构复杂,在充分发挥计算能力和提高计算效率方面存在较大挑战。尤其针对有别于科学计算的人工智能和大数据领域,其并行计算效率的提升更为困难。【结论】因此需要从底层的资源管理、任务调度、以及基础算法设计、通信优化,到上层的模型并行化和并行编程等方面展开高效能计算的研究,全面提升人工智能和大数据应用在高性能计算平台上的计算能效。     

EXPERT SYSTEMS CONNECTIVITY

Abstract

The commercial success of all artificial intelligence applications depends significantly on their ability to communicate with the currently existing data and applications environment. This article attempts to provide an overview of certain minimal requirements of both data processing and artificial intelligence applications and their implementation, which at least must be satisfied to achieve problem-free integration.     

可解释的知识图谱推理方法综述

近年来,以深度学习模型为基础的人工智能研究不断取得突破性进展,但其大多具有黑盒性,不利于人类认知推理过程,导致高性能的复杂算法、模型及系统普遍缺乏决策的透明度和可解释性。在国防、医疗、网络与信息安全等对可解释性要求严格的关键领域,推理方法的不可解释性对推理结果及相关回溯造成较大影响,因此,需要将可解释性融入这些算法和系统中,通过显式的可解释知识推理辅助相关预测任务,形成一个可靠的行为解释机制。知识图谱作为最新的知识表达方式之一,通过对语义网络进行建模,以结构化的形式描述客观世界中实体及关系,被广泛应用于知识推理。基于知识图谱的知识推理在离散符号表示的基础上,通过推理路径、逻辑规则等辅助手段,对推理过程进行解释,为实现可解释人工智能提供重要途径。针对可解释知识图谱推理这一领域进行了全面的综述。阐述了可解释人工智能和知识推理相关概念。详细介绍近年来可解释知识图谱推理方法的最新研究进展,从人工智能的3个研究范式角度出发,总结了不同的知识图谱推理方法。提出对可解释的知识图谱推理研究前景和未来研究方向。     

无人系统的视觉感知安全研究

随着现代科技的不断革新,以机器学习尤其是深度学习为代表的人工智能技术正在改变无人系统的发展,推动无人作战等作战形态快速演变,对未来战争带来颠覆性影响。然而由于深度学习的不可解释性、脆弱性等问题,人工智能技术在现实应用中产生了诸多不确定性和安全风险。本文聚焦人工智能技术在军事无人系统中的安全问题,从视觉感知的角度出发,重点分析了安全风险来源、对抗样本理论和视觉感知对抗攻击方法和防御对策,最后对无人系统领域人工智能应用的安全问题进行了总结。     

最短路径搜索的实现及算法优化

在游戏开发过程中常常面临多条通路存在却不知道应该选择哪条路径到达终点效率最高的问题。本文应用人工智能中的路径搜索算法将这一问题的求解过程转化为在状态空间中从初始状态到目标状态寻找路径的过程。在寻找最佳路径的过程中,应用启发式搜索和估价函数,对每一个搜索的位置先进行评估,从而得到最好的起始点,再从这个位置依次搜索直到目标,最终发现到达终点的最便捷途径。同时结合实际应用中遇到的问题,给出了该算法的进一步优化解决方案。     

Handling non-canonical software requirements based on Annotated Predicate Calculus

Eliciting requirements for a proposed system inevitably involves the problem of handling undesirable information about customer's needs, including inconsistency, vagueness, redundancy, or incompleteness. We term the requirements statements involved in the undesirable information non-canonical software requirements. In this paper, we propose an approach to handling non-canonical software requirements based on Annotated Predicate Calculus (APC). Informally, by defining a special belief lattice appropriate for representing the stakeholder's belief in requirements statements, we construct a new form of APC to formalize requirements specifications. We then show how the APC can be employed to characterize non-canonical requirements. Finally, we show how the approach can be used to handle non-canonical requirements through a case study. Kedian Mu received B.Sc. degree in applied mathematics from Beijing Institute of Technology, Beijing, China, in 1997, M.Sc. degree in probability and mathematical statistics from Beijing Institute of Technology, Beijing, China, in 2000, and Ph.D. in applied mathematics from Peking University, Beijing, China, in 2003. From 2003 to 2005, he was a postdoctoral researcher at Institute of Computing Technology, Chinese Academy of Sciences, China. He is currently an assistant professor at School of Mathematical Sciences, Peking University, Beijing, China. His research interests include uncertain reasoning in artificial intelligence, knowledge engineering and science, and requirements engineering. Zhi Jin was awarded B.Sc. in computer science from Zhejiang University, Hangzhou, China, in 1984, and studied for her M.Sc. in computer science (expert system) and her Ph.D. in computer science (artificial intelligence) at National Defence University of Technology, Changsha, China. She was awarded Ph.D. in 1992. She is a senior member of China Computer Federation. She is currently a professor at Academy of Mathematics and System Sciences, Chinese Academy of Science. Her research interests include knowledge-based systems, artificial intelligence, requirements engineering, ontology engineering, etc. Her current research focuses on ontology-based requirements elicitation and analysis. She has got about 60 papers published, including co-authoring one book. Ruqian Lu is a professor of computer science of the Institute of Mathematics, Chinese Academy of Sciences. His research interests include artificial intelligence, knowledge engineering and knowledge based software engineering. He designed the “Tian Ma” software systems that have been widely applied in more than 20 fields, including the national defense and the economy. He has won two first class awards from Chinese Academy of Sciences and a National second class prize from the Ministry of Science and Technology. He has also won the sixth Hua Lookeng Prize for Mathematics. Yan Peng received B.Sc. degree in software from Jilin University, Changchun, China, in 1992. From June 2002 to December 2005, he studied for his M.E. in software engineering at College of Software Engineering, Graduate School of Chinese Academy of Sciences, Beijing, China. He was awarded M.E degree in 2006. He is currently responsible for CRM (customer relationship management) and BI (business intelligence) project in the BONG. His research interests include customer relationship management, business intelligence, data ming, software engineering and requirements engineering.     

面向边缘计算应用的宽度孪生网络

边缘计算是将计算、存储、通信等任务分配到网络边缘的计算模式. 它强调在用户终端附近执行数据处理过程, 以达到降低延迟, 减少能耗, 保护用户隐私等目的. 然而网络边缘的计算、存储、能源资源有限, 这给边缘计算应用的推广带来了新的挑战. 随着边缘智能的兴起, 人们更希望将边缘计算应用与人工智能技术结合起来, 为我们的生活带来更多的便利. 许多人工智能方法, 如传统的深度学习方法, 需要消耗大量的计算、存储资源, 并且伴随着巨大的时间开销. 这不利于强调低延迟的边缘计算应用的推广. 为了解决这个问题, 我们提出将宽度学习系统(Broad learning system, BLS)等浅层网络方法应用到边缘计算应用领域, 并且设计了一种宽度孪生网络算法. 我们将宽度学习系统与孪生网络结合起来用于解决分类问题. 实验结果表明我们的方法能够在取得与传统深度学习方法相似精度的情况下降低时间和资源开销, 从而更好地提高边缘计算应用的性能.     

Artificial Driving Intelligence and Moral Agency: Examining the Decision Ontology of Unavoidable Road Traffic Accidents through the Prism of the Trolley Dilemma

The question of the capacity of artificial intelligence to make moral decisions has been a key focus of investigation in robotics for decades. This question has now become pertinent to automated vehicle technologies, as a question of understanding the capacity of artificial driving intelligence to respond to unavoidable road traffic accidents. Artificial driving intelligence will make a calculated decision that could equate to deciding who lives and who dies. In calculating such important decisions, does the driving intelligence require moral intelligence and a capacity to make informed moral decisions? Artificial driving intelligence will be determined by at very least, state laws, driving codes, and codes of conduct relating to driving behaviour and safety. Does it also need to be informed by ethical theories, human values, and human rights frameworks? If so, how can this be achieved and how can we ensure there are no moral biases in the moral decision-making algorithms? The question of moral capacity is complex and has become the ethical focal point of this technology. Research has centred on applying Philippa Foot’s famous trolley dilemma. We claim that before applications attempt to focus on moral theories, there is a necessary precedent to utilise the trolley dilemma as an ontological experiment. The trolley dilemma is succinct in identifying important ontological differences between human driving intelligence and artificial driving intelligence. In this paper, we argue that when the trolley dilemma is focused upon ontology, it has the potential to become an important elucidatory tool. It can act as a prism through which one can perceive different ontological aspects of driving intelligence and assess response decisions to unavoidable road traffic accidents. The identification of the ontological differences is integral to understanding the underlying variances that support human and artificial driving decisions. Ontologically differentiating between these two contexts allows for a more complete interrogation of the moral decision-making capacity of the artificial driving intelligence.     

建筑电气系统故障诊断综述

从建筑电气系统研究现状、应用前景以及故障诊断方法等方面进行阐述;介绍信号分析、解析模型和人工智能算法三大类中用于解决建筑电气系统故障诊断的典型算法;综述三类方法在故障诊断中的模型选择、学习算法和实际应用等方面的研究进展;探讨信号分析、解析模型和人工智能算法在建筑电气系统故障诊断中的理论分析,以及待解决的问题和未来的发展前景等。     

Artificial intelligence test: a case study of intelligent vehicles

To meet the urgent requirement of reliable artificial intelligence applications, we discuss the tight link between artificial intelligence and intelligence test in this paper. We highlight the role of tasks in intelligence test for all kinds of artificial intelligence. We explain the necessity and difficulty of describing tasks for intelligence test, checking all the tasks that may encounter in intelligence test, designing simulation-based test, and setting appropriate test performance evaluation indices. As an example, we present how to design reliable intelligence test for intelligent vehicles. Finally, we discuss the future research directions of intelligence test.