刘乃军, 鲁涛, 蔡莹皓, 王硕. 机器人操作技能学习方法综述. 自动化学报, 2019, 45(3): 458-470.
欧阳丽炜, 王帅, 袁勇, 倪晓春, 王飞跃. 智能合约:架构及进展. 自动化学报, 2019, 45(3): 445-457.
关新平, 陈彩莲, 杨博, 华长春, 吕玲, 朱善迎. 工业网络系统的感知-传输-控制一体化:挑战和进展. 自动化学报, 2019, 45(1): 25-36.
胡建芳, 王熊辉, 郑伟诗, 赖剑煌. RGB-D行为识别研究进展及展望. 自动化学报, 2019, 45(5): 829-840.
李洪阳, 魏慕恒, 黄洁, 邱伯华, 赵晔, 骆文城, 何晓, 何潇. 信息物理系统技术综述. 自动化学报, 2019, 45(1): 37-50.
黄雅婷, 石晶, 许家铭, 徐波. 鸡尾酒会问题与相关听觉模型的研究现状与展望. 自动化学报, 2019, 45(2): 234-251.
刘烃, 田决, 王稼舟, 吴宏宇, 孙利民, 周亚东, 沈超, 管晓宏. 信息物理融合系统综合安全威胁与防御研究. 自动化学报, 2019, 45(1): 5-24.
Networked Control Systems: A Survey of Trends and Techniques
Networked control systems are spatially distributed systems in which the communication between sensors, actuators, and controllers occurs through a shared band-limited digital communication network. Several advantages of the network architectures include reduced system wiring, plug and play devices, increased system agility, and ease of system diagnosis and maintenance. Consequently, networked control is the current trend for industrial automation and has ever-increasing applications in a wide range of areas, such as smart grids, manufacturing systems, process control, automobiles, automated highway systems, and unmanned aerial vehicles. The modelling, analysis, and control of networked control systems have received considerable attention in the last two decades. The ‘control over networks’ is one of the key research directions for networked control systems. This paper aims at presenting a survey of trends and techniques in networked control systems from the perspective of ‘control over networks’, providing a snapshot of five control issues: sampleddata control, quantization control, networked control, eventtriggered control, and security control. Some challenging issues are suggested to direct the future research.
X.-M. Zhang, Q.-L. Han, X. H. Ge, D. Ding, L. Ding, D. Yue, and C. Peng, “Networked control systems: a survey of trends and techniques,” IEEE/CAA J. Autom. Sinica, doi: 10.1109/JAS.2019.1911651
An Overview and Perspectives On Bidirectional Intelligence: Lmser Duality, Double IA Harmony, and Causal Computation
Advances on bidirectional intelligence are overviewed along three threads, with extensions and new perspectives. The first thread is about bidirectional learning architecture, exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lot of extensions and particularlly flexible Lmser are proposed. Interestingly, either or two of these dualities actually takes an important role in recent models such as U-net, ResNet, and DenseNet. The second thread is about bidirectional learning principles unified by best yIng-yAng (IA) harmony in BYY system. After getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions, maximum likelihood, variational principle, and several other learning principles are summarised as exemplars of the BYY learning, with new perspectives on advanced topics. The third thread further proceeds to deep bidirectional intelligence, driven by long term dynamics (LTD) for parameter learning and short term dynamics (STD) for image thinking and rational thinking in harmony. Image thinking deals with information flow of continuously valued arrays and especially image sequence, as if thinking was displayed in the real world, exemplified by the flow from inward encoding/cognition to outward reconstruction/transformation performed in Lmser learning and BYY learning. In contrast, rational thinking handles symbolic strings or discretely valued vectors, performing uncertainty reasoning and problem solving. In particular, a general thesis is proposed for bidirectional intelligence, featured by BYY intelligence potential theory (BYY-IPT) and nine essential dualities in architecture, fundamentals, and implementation, respectively. Then, problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT perspective. First, variants and extensions are suggested for AlphaGoZero like searching tasks, such as traveling salesman problem (TSP) and attributed graph matching (AGM) that are turned into Go like problems with help of a feature enrichment technique. Second, reasoning activities are summarized under guidance of BYY IPT from the aspects of constraint satisfaction, uncertainty propagation, and path or tree searching. Particularly, causal potential theory is proposed for discovering causal direction, with two roads developed for its implementation.
L. Xu, “An overview and perspectives on bidirectional intelligence: Lmser duality, double IA harmony, and causal computation,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 865–893, Jul. 2019.
A Survey of Multi-robot Regular and Adversarial Patrolling
Multi-robot systems can be applied to patrol a concerned environment for security purposes. According to different goals, this work reviews the existing researches in a multi-robot patrolling field from the perspectives of regular and adversarial patrolling. Regular patrolling requires robots to visit important locations as frequently as possible and a series of deterministic strategies are proposed, while adversarial one focuses on unpredictable robots’ moving patterns to maximize adversary detection probability. Under each category, a systematic survey is done including problem statements and modeling, patrolling objectives and evaluation criteria, and representative patrolling strategies and approaches. Existing problems and open questions are presented accordingly.
L. Huang, M. C. Zhou, K. R. Hao, and E. Hou, “A survey of multi-robot regular and adversarial patrolling,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 894–903, Jul. 2019
The Need for Fuzzy AI
Artificial intelligence (AI) is once again a topic of huge interest for computer scientists around the world. Whilst advances in the capability of machines are being made all around the world at an incredible rate, there is also increasing focus on the need for computerised systems to be able to explain their decisions, at least to some degree. It is also clear that data and knowledge in the real world are characterised by uncertainty. Fuzzy systems can provide decision support, which both handle uncertainty and have explicit representations of uncertain knowledge and inference processes. However, it is not yet clear how any decision support systems, including those featuring fuzzy methods, should be evaluated as to whether their use is permitted. This paper presents a conceptual framework of indistinguishability as the key component of the evaluation of computerised decision support systems. Case studies are presented in which it has been clearly demonstrated that human expert performance is less than perfect, together with techniques that may enable fuzzy systems to emulate human-level performance including variability. In conclusion, this paper argues for the need for " fuzzy AI” in two senses: (i) the need for fuzzy methodologies (in the technical sense of Zadeh’s fuzzy sets and systems) as knowledge-based systems to represent and reason with uncertainty; and (ii) the need for fuzziness (in the non-technical sense) with an acceptance of imperfect performance in evaluating AI systems.
J. M. Garibaldi, “The need for fuzzy AI,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 610–622, May 2019.
Advances in Control Technologies for Wastewater Treatment Processes:Status, Challenges, and Perspectives
This paper presents a thorough review of control technologies that have been applied to wastewater treatment processes in the environmental engineering regime in the past four decades. It aims to provide a comprehensive technological review for both water engineering professionals and control specialists, giving rise to a suite of up-to-date pathways to impact this field in light of the classified technology hubs. The assessment was conducted with respect to linear control, linearizing control, nonlinear control, and artificial intelligence-based control. The application domain of each technology hub was summarized into a set of comparative tables for a holistic assessment. Challenges and perspectives were offered to these field engineers to help orient their future endeavor.
A. Iratni and N.-B. Chang, “Advances in control technologies for wastewater treatment processes: status, challenges, and perspectives,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 337-363, Mar. 2019.
Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations
In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.
D. P. Bertsekas, “Feature-based aggregation and deep reinforcement learning: a survey and some new implementations,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 1-31, Jan. 2019.