Research on advanced soft computing and its applications

The main objective for the research presented in this special issue is to advance theoretical basis in soft computing, for the purpose of improving applications. Why is this theoretical research needed? Because soft computing in general (and intelligent control and decision making in particular) are, in many aspects, still an art. To make this methodology easier to apply, we must use the experience of successful applications of fuzzy control, decision making or classification and extract formal rules that would capture this experience. To be able to do that efficiently, we must understand why some versions of soft computing methodology turned out to be more successful in some practical situations and less successful in others. In other words, to advance the practical success of soft computing methodology, we need further theoretical analysis of soft computing – analysis targeted at enhancing its application abilities.     

Artificial Intelligence Applications in the Development of Autonomous Vehicles: A Survey

The advancement of artificial intelligence (AI) has truly stimulated the development and deployment of autonomous vehicles (AVs) in the transportation industry. Fueled by big data from various sensing devices and advanced computing resources, AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion. To achieve goal of full automation (i.e., self-driving), it is important to know how AI works in AV systems. Existing research have made great efforts in investigating different aspects of applying AI in AV development. However, few studies have offered the research community a thorough examination of current practices in implementing AI in AVs. Thus, this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue. Specifically, it intends to analyze their use of AIs in supporting the primary applications in AVs: 1) perception; 2) localization and mapping; and 3) decision making. It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation. Based on the exploration of current practices and technology advances, this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies: 1) high definition maps, big data, and high performance computing; 2) augmented reality (AR)/virtual reality (VR) enhanced simulation platform; and 3) 5G communication for connected AVs. This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.      

自动化学科面临的挑战

本文分析了控制理论与应用、模式识别与智能系统、导航制导与控制、系统科学与工程、人工智能与自动化交叉等领域的发展现状. 结合科技发展、国内国际研究前沿和新兴领域对自动化科学技术的需求, 提出重点发展智能控制理论和方法、高性能作业机器人、信息物理系统、导航与控制技术、重大装备自动化技术、自主智能系统和人工智能驱动的自动化技术优先领域, 加强数据驱动控制理论、人工智能基础理论研究, 进一步发展人机协同、跨域融合的智能自动化, 为实现国家社会的全面信息化智能化提供理论和技术保障.     

Misuse of automated decision aids: Complacency, automation bias and the impact of training experience

The present study investigates automation misuse based on complacency and automation bias in interacting with a decision aid in a process control system. The effect of a preventive training intervention which includes exposing participants to rare automation failures is examined. Complacency is reflected in an inappropriate checking and monitoring of automated functions. In interaction with automated decision aids complacency might result in commission errors, i.e., following automatically generated recommendations even though they are false. Yet, empirical evidence proving this kind of relationship is still lacking. A laboratory experiment (N=24) was conducted using a process control simulation. An automated decision aid provided advice for fault diagnosis and management. Complacency was directly measured by the participants’ information sampling behavior, i.e., the amount of information sampled in order to verify the automated recommendations. Possible commission errors were assessed when the aid provided false recommendations. The results provide clear evidence for complacency, reflected in an insufficient verification of the automation, while commission errors were associated with high levels of complacency. Hence, commission errors seem to be a possible, albeit not an inevitable consequence of complacency. Furthermore, exposing operators to automation failures during training significantly decreased complacency and thus represents a suitable means to reduce this risk, even though it might not avoid it completely. Potential applications of this research include the design of training protocols in order to prevent automation misuse in interaction with automated decision aids.     

智能软件定义网络

近年来,人工智能(artificial intelligence,简称AI)以强劲势头吸引着学术界和工业界的目光,并被广泛应用于各种领域.计算机网络为人工智能的实现提供了关键的计算基础设施.然而,传统网络固有的分布式结构往往无法快速、精准地提供人工智能所需要的计算能力,导致人工智能难以实际应用和部署.软件定义网络(so...     

The role of trust in automation reliance

A recent and dramatic increase in the use of automation has not yielded comparable improvements in performance. Researchers have found human operators often underutilize (disuse) and overly rely on (misuse) automated aids (Parasuraman and Riley, 1997). Three studies were performed with Cameron University students to explore the relationship among automation reliability, trust, and reliance. With the assistance of an automated decision aid, participants viewed slides of Fort Sill terrain and indicated the presence or absence of a camouflaged soldier. Results from the three studies indicate that trust is an important factor in understanding automation reliance decisions. Participants initially considered the automated decision aid trustworthy and reliable. After observing the automated aid make errors, participants distrusted even reliable aids, unless an explanation was provided regarding why the aid might err. Knowing why the aid might err increased trust in the decision aid and increased automation reliance, even when the trust was unwarranted. Our studies suggest a need for future research focused on understanding automation use, examining individual differences in automation reliance, and developing valid and reliable self-report measures of trust in automation.     

知识可编程智能芯片系统:概念、架构与展望

本文原创性地提出知识可编程智能芯片系统(KPI-CS)及其理论和工程体系.该系统在当前最先进的异构计算和可重构人工智能(AI)芯片技术的基础上,深度融合复杂系统工程理论、知识工程理论与技术、半导体芯片研发技术、人工智能可重构算法技术,提出基于知识的可重构智能芯片和计算系统平台技术.该系统旨在支持AI应用场景适应性、AI系统重构灵活性、AI算法算力合理性的平行智能AI芯片系统平台和对应的知识服务平台.同时,作为应用展望,KPI-CS与相应的应用平台联动,为平行复杂系统管理与控制、智能交通、智能能源、平行区块链、智能医疗等研究领域和工程实践提供新一代的实时、高效、自适应的计算系统支撑.     

Intelligent engineering software research at the Tallinn Institute of Cybernetics

The Institute of Cybernetics of the Estonian Academy of Sciences is a centre for research in intelligent software, and applications of AI in engineering. One goal of the research is to develop intelligent software environments and expert system shells for workstations and personal computers which will be easy to use for engineering problems. A set of program packages has been developed for mechanical engineering as well as for electronics, using intelligent software systems MicroPRIZ and MicroEXPERT.     

Intelligent systems in the automotive industry: applications and trends

There is a common misconception that the automobile industry is slow to adapt new technologies, such as artificial intelligence (AI) and soft computing. The reality is that many new technologies are deployed and brought to the public through the vehicles that they drive. This paper provides an overview and a sampling of many of the ways that the automotive industry has utilized AI, soft computing and other intelligent system technologies in such diverse domains like manufacturing, diagnostics, on-board systems, warranty analysis and design. Oleg Gusikhin received the Ph.D. degree from St. Petersburg Institute of Informatics and Automation of the Russian Academy of Sciences and the M.B.A. degree from the University of Michigan, Ann Arbor, MI. Since 1993, he has been with the Ford Motor Company, where he is a Technical Leader at the Ford Manufacturing and Vehicle Design Research Laboratory, and is engaged in different functional areas including information technology, advanced electronics manufacturing, and research and advanced engineering. He has also been involved in the design and implementation of intelligent control applications for manufacturing and vehicle systems. He is the recipient of the 2004 Henry Ford Technology Award. He holds two U.S. patents and has published over 30 articles in refereed journals and conference proceedings. He is an Associate Editor of the International Journal of Flexible Manufacturing Systems. He is also a Certified Fellow of the American Production and Inventory Control Society and a member of IEEE and SME. Nestor Rychtyckyj received the Ph.D. degree in computer science from Wayne State University, Detroit, MI. He is a technical expert in Artificial Intelligence at Ford Motor Company, Dearborn, MI, in Advanced and Manufacturing Engineering Systems. His current research interests include the application of knowledge-based systems for vehicle assembly process planning and scheduling. Currently, his responsibilities include the development of automotive ontologies, intelligent manufacturing systems, controlled languages, machine translation and corporate terminology management. He has published more than 30 papers in referred journals and conference proceedings. He is a member of AAAI, ACM and the IEEE Computer Society. Dimitar P. Filev received the Ph.D. degree in electrical engineering from the Czech Technical University, Prague, in 1979. He is a Senior Technical Leader, Intelligent Control and Information Systems with Ford Research and Advanced Engineering specializing in industrial intelligent systems and technologies for control, diagnostics and decision making. He is conducting research in systems theory and applications, modeling of complex systems, intelligent modeling and control, and has published 3 books and over 160 articles in refereed journals and conference proceedings. He holds 14 granted U.S. patents and numerous foreign patents in the area of industrial intelligent systems He is the recipient of the 1995 Award for Excellence of MCB University Press. He was awarded the Henry Ford Technology Award four times for development and implementation of advanced intelligent control technologies. He is an Associate Editor of International Journal of General Systems and International Journal of Approximate Reasoning. He is a member of the Board of Governors of the IEEE Systems, Man and Cybernetics Society and President of the North American Fuzzy Information Processing Society (NAFIPS).     

Rapid goal-oriented automated software testing using MEA-graph planning

With the rapid growth in the development of sophisticated modern software applications, the complexity of the software development process has increased enormously, posing an urgent need for the automation of some of the more time-consuming aspects of the development process. One of the key stages in the software development process is system testing. In this paper, we evaluate the potential application of AI planning techniques in automated software testing. The key contributions of this paper include the following: (1) A formal model of software systems from the perspective of software testing that is applicable to important classes of systems and is amenable to automation using AI planning methods. (2) The design of a framework for an automated planning system (APS) for applying AI planning techniques for testing software systems. (3) Assessment of the test automation framework and a specific AI Planning algorithm, namely, MEA-Graphplan (Means-Ends Analysis Graphplan), algorithm to automatically generate test data. (4) A case study is presented to evaluate the proposed automated testing method and compare the performance of MEA-Graphplan with that of Graphplan. The empirical results show that for software testing, the MEA-Graphplan algorithm can perform computationally more efficiently and effectively than the basic Graph Planning algorithm.

工业自动化控制系统的设计

工业生产过程自动化系统经过长期不断的发展,特别是在充分利用计算机技术的基础上取得了很大的进步,在生产过程中已发挥其重要作用,成为生产过程安全、稳定、自动化运行不可缺少的工具。本文从工业生产自动化现状趋势、生产过程自动化系统和生产管理系统、软PLC和软DCS、生产过程控制和管理软件的融合等方面展开论述．从工业生产与自动化控制融合环节提出工业自动化控制系统设计的独特见解。     

IT应用运维自动化研究与应用

本文就IT应用运维的现状、运维对象、工作内容以及面临的挑战进行分析,有针对性的就如何提高IT应用运维的自动化程度给出了建议和方案,并扼要介绍运维自动化带来的效果.     

工业自动化控制系统的设计

工业生产过程自动化系统经过长期不断的发展,特别是在充分利用计算机技术的基础上取得了很大的进步,在生产过程中已发挥其重要作用,成为生产过程安全、稳定、自动化运行不可缺少的工具。本文从工业生产自动化现状趋势、生产过程自动化系统和生产管理系统、软PLC和软DCS、生产过程控制和管理软件的融合等方面展开论述,从工业生产与自动化控制融合环节提出工业自动化控制系统设计的独特见解。     

基于科研知识图谱的近年国内外自动化学科发展综述

现今,以自动控制和信息处理为核心的自动化技术已经成为推动生产力发展、改善人类生活以及促进社会前进的源动力之一.全面了解自动化学科的最新发展态势,对本领域科研部门、科研人员进行工作的规划与实施有着重要的参考价值,本文以2011年～2013年期间88种期刊的46242篇文章作为数据基础,采用文献计量学、社会网络分析等方法进行数据解析,通过知识图谱定量描绘出本领域5个方向(控制理论与控制工程、模式识别与智能系统、系统工程、检测技术与自动化装置、导航、制导与控制)的最新研究态势.结果表明,本领域国内研究热点与国际研究热点各有侧重,国内机构在国际研究中的地位逐步提高,特别地,华人群体在本领域的研究中起到重要的推动作用.     

智能时代的汽车控制

自动驾驶是汽车产业发展的重要里程碑. 汽车驾驶自动化一直都在进行, 其发展进程是对驾驶人认知感知、决策规划和执行控制等各个重要环节的逐步增强或最终替代. 智能时代下, 大数据分析、泛在计算、泛在传感和人工智能等颠覆性技术为汽车驾驶自动化向着高级别迈进提供了新的机遇. 控制技术是智能时代汽车自动化进程中的基石, 更多的信息在先进控制技术的赋能下将衍生出更多的新功能与新系统, 从而实现汽车安全性、经济性以及舒适性等各个方面的提升. 本文对智能时代的汽车控制进行综述, 首先回顾汽车自动化的发展进程, 然后探讨汽车自动化进程中面临的问题, 最后梳理出一些未来智能汽车控制发展趋势与关键技术.     

A Misdirected Principle with a Catch: Explicability for AI

There is widespread agreement that there should be a principle requiring that artificial intelligence (AI) be ‘explicable’. Microsoft, Google, the World Economic Forum, the draft AI ethics guidelines for the EU commission, etc. all include a principle for AI that falls under the umbrella of ‘explicability’. Roughly, the principle states that “for AI to promote and not constrain human autonomy, our ‘decision about who should decide’ must be informed by knowledge of how AI would act instead of us” (Floridi et al. in Minds Mach 28(4):689–707, 2018). There is a strong intuition that if an algorithm decides, for example, whether to give someone a loan, then that algorithm should be explicable. I argue here, however, that such a principle is misdirected. The property of requiring explicability should attach to a particular action or decision rather than the entity making that decision. It is the context and the potential harm resulting from decisions that drive the moral need for explicability—not the process by which decisions are reached. Related to this is the fact that AI is used for many low-risk purposes for which it would be unnecessary to require that it be explicable. A principle requiring explicability would prevent us from reaping the benefits of AI used in these situations. Finally, the explanations given by explicable AI are only fruitful if we already know which considerations are acceptable for the decision at hand. If we already have these considerations, then there is no need to use contemporary AI algorithms because standard automation would be available. In other words, a principle of explicability for AI makes the use of AI redundant.

     

Parallel Driving in CPSS:A Unified Approach for Transport Automation and Vehicle Intelligence

The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems (CPSS) framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space, considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon (iHorizon) and its applications are also presented towards parallel horizon. The proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.