Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

With integration of large-scale renewable energy,new controllable devices,and required reinforcement of power grids,modern power systems have typical characteristics such as uncertainty,vulnerability and openness,which makes operation and control of power grids face severe security challenges.Application of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex modeling.Mode of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this field.Based on characteristics of applications in power grid regulation,this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence(HHI)system for large-scale power grid dispatching and control(PGDC).First,theory and application scenarios of HHI are introduced and analyzed;then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed.Key technologies are discussed to achieve a thorough integration of human/machine intelligence.Finally,state-of-theart and future development of HHI in power grid regulation are summarized,aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

Human-Machine Shared Lateral Control Strategy for Intelligent Vehicles Based on Human Driver Risk Perception Reliability

Intelligent vehicle(Ⅳ)technology has developed rapidly in recent years.However,achieving fully unmanned driving still presents numerous challenges,which means that human drivers will continue to play a vital role in vehicle operation for the foreseeable future.Human-machine shared driving,involving cooperation between a human driver and an automated driving system(AVS),has been widely regarded as a necessary stage for the development of IVs.Focusing onⅣdriving safety,this study proposed a human-machine shared lateral control strategy(HSLCS)based on the reliability of driver risk perception.The HSLCS starts by identifying the effective areas of driver risk perception based on eye movements.It establishes an anisotropic driving risk field,which serves as the foundation for the AVS to assess risk levels.Building upon the cumulative and diminishing effects of risk perception,the proposed approach leverages the driver's risk perception effective area and converts the risk field into a representation aligned with the driver's perspective.Subsequently,it quantifies the reliability of the driver's risk perception by using area-matching rules.Finally,based on the driver’s risk perception reliability and dif-ferences in lateral driving operation between the human driver and the AVS,the dynamic distribution of driving authority is achieved through a fuzzy rule-based system,and the human-machine shared lateral control is completed by using model predictive control.The HSLCS was tested across various scenarios on a driver-in-the-loop test platform.The results show that the HSLCS can realize the synergy and complementarity of human and machine intelligence,effectively ensuring the safety ofⅣoperation.

Swarm Intelligence Research:From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

Swarm intelligence has become a hot research field of artificial intelligence.Considering the importance of swarm intelli-gence for the future development of artificial intelligence,we discuss and analyze swarm intelligence from a broader and deeper perspect-ive.In a broader sense,we are talking about not only bio-inspired swarm intelligence,but also human-machine hybrid swarm intelli-gence.In a deeper sense,we discuss the research using a three-layer hierarchy:in the first layer,we divide the research of swarm intelli-gence into bio-inspired swarm intelligence and human-machine hybrid swarm intelligence;in the second layer,the bio-inspired swarm intelligence is divided into single-population swarm intelligence and multi-population swarm intelligence;and in the third layer,we re-view single-population,multi-population and human-machine hybrid models from different perspectives.Single-population swarm intel-ligence is inspired by biological intelligence.To further solve complex optimization problems,researchers have made preliminary explor-ations in multi-population swarm intelligence.However,it is difficult for bio-inspired swarm intelligence to realize dynamic cognitive in-telligent behavior that meets the needs of human cognition.Researchers have introduced human intelligence into computing systems and proposed human-machine hybrid swarm intelligence.In addition to single-population swarm intelligence,we thoroughly review multi-population and human-machine hybrid swarm intelligence in this paper.We also discuss the applications of swarm intelligence in optimization,big data analysis,unmanned systems and other fields.Finally,we discuss future research directions and key issues to be studied in swarm intelligence.

Interaction and the Genesis of Experience: A Phenomenological Contribution for Meaningful Embodied Artificial Intelligence

In this article I will address the issue of the meaning of Embodied Artificial Intelligence(EAI)as it is configured today.My starting point is the refined interactive perspective on the semantics of EAI,as was recently suggested by Froese and colleagues.This perspective rests on the assumption that the concept of human bodily subjectivity must be extended to include meaning-making processes,which are enabled by advanced AI systems that may be incorporated in the human biological body.After having clarified the technical background,I will introduce the genetic component of the phenomenological method as a suitable tool to face the aforementioned issue.Towards this end,I will place the genetic method in the context of the so-called New Human-Machine Interaction(New HMI).I will further outline a genetic phenomenology of visual embodiment,suggesting a futuristic application based on the thesis of the“technological supplementation of phenomenological methodology”through the synthetic method.The case at stake is that of patients with a severe clinical picture characterised by the loss of corneal function,who in the near future could be treated with synthetic corneal prosthetic implants produced by a 3D bio-printing process by using an advanced EAI technique.I will conclude this article with a brief review of the main problems that still remain open.

人智交互情境中用户对生成式人工智能的心智感知及反应研究

[目的/意义]随着AI组成技术的发展及人智协作的日趋紧密,高阶的生成式人工智能(GAI)代理具备涌现能力,通过类人化特征与用户建立社会化连接。文章旨在丰富社会技术视角下人智交互发生机理的探索,并为人本人工智能背景下GAI设计提供参考。[方法/过程]爬取豆瓣社区1275条用户与ChatGPT对话的帖子,遵循建构主义范式,基于CASA范式和心智感知理论,采用系统性主题分析方法构建框架模型。[结果/结论]构建了GAI作为社会行动者的理论框架,得到人智交互情境下用户对于GAI的心智感知及社会化反应。心智感知包括GAI主体性感知和感受性感知两种。GAI主体性感知包括创造力、可靠性、运行规则、客观性、有用性;GAI感受性感知包括共情、沟通责任、趣味性、友善、文化适应性。这两种感知会相互影响。用户对GAI的社会化反应涉及认知加工、情感唤醒、行为强化三个方面。认知加工包括知识获得、态度转变;情感唤醒包括愉悦感、共鸣、社交连接;行为强化包括持续付费行为、准社会互动。用户的社会化反应与心智感知存在双向作用关系。

拓维与共生:数智时代高校教学评价体系建设的高质量发展路径

高校教学评价是高校评判教学工作、回归教学本位、深化教学改革和提升教学质量的关键环节。在数智时代,高校教学评价体系面临“师-生”“校-社”“教-创”“学-用”等四个维度的拓展。运用共生理论的最优评价标准进行审视发现,高校教学评价体系存在教学主体缺位、教学功能异化、教学关系非对称性与教学效果非互惠性的现实困境。由此,高校教学评价体系的高质量发展路径为:建立数智型教学评价制度,抬升弱势教学主体评价;塑造高质量的教学评价观,发挥教学评价的数智功能;推动教学评价管理数智化,拓宽多元主体互评渠道;构建并喻文化的互惠模型,实现高质量的教智融合评价。

人机共生中的拟人化:概念溯源、谱系重建与议题拓展

拟人化,是将人类特征赋予非人能动体的过程。拟人化不仅影响了个体在人机传播中的情感及认知,更作为一种“意义生成”被卷入人机共生的谱系中。研究对拟人化的概念及对象进行溯源,并对拟人化的具体维度进行梳理。而在现有研究中,拟人化作为人机传播中常见的心理现象,多被视为被机器线索触发的无意识互动,伴随着人机之间的长期关系成为可能,机器的类人能力也对人的独特性形成潜在威胁。研究将拟人化的研究谱系基于“价值共生”与“长期关系”进行拓展,并从拟人化与情感关系调适、拟人化与共生意义建构、拟人化与技术环境适应三个方向,对相关研究议题进行具体解释。研究认为,拟人化能为人机情感关系提供往返和调适,催生新的人机传播脚本,并在共生意义层面协助个体进行自我的“分布式”建构,而不同群体的文化实践也为拟人化提供了多元的解释资源,面对持续变化且充满不确定性的技术环境,拟人化也能为个体提供适应经验。

人机共生:人工智能驱动下艺术设计的发展与革新

如今,人类站在了人机共生时代历史性的转折点上,人工智能已逐渐融入艺术设计领域,过去的设计范式即将面临消解,新的设计范式悄然建立。艺术设计在人工智能面前面临着巨大挑战,未来的艺术设计如何与人工智能共存是值得思考的问题。本文通过归纳案例从人工智能与艺术设计的现状出发,探究艺术设计领域的未来发展与革新,总结出在人机共生背景下人工智能与设计双向赋能并相互引领两个行业未来的变革与潮流。

智能化教学评价的风险挑战及对策研究

数智时代,人工智能赋能教学评价迎来了可喜的变化,其主要表现在教学评价主体多元化、评价方式多样化、评价过程动态化和评价结果个性化四个方面。目前,智能化教学评价工具仍存在一些伦理性风险,如:评价安全风险、忽视复杂质性因素和忽视评价主体尊严等,因此,评价主体在评价过程中应保持理性和谨慎。同时,有关院校也应制定严格的数据安全规约、构建人本化的评价伦理秩序、发挥协同化的双向联动效应,使智能化技术与教学评价深度耦合,发挥数智技术“向善”的功用,营造人与技术协同共生的有序评价氛围,创设智能化教学评价的生态化发展新空间。

基于人工智能技术的播音主持“人机共生”实现策略

人工智能(Artificial Intelligence,AI)技术为媒介传播活动提供了强大的技术支撑,AI虚拟主播应运而生,在媒介传播中参与度越来越高,不仅改变了新闻播报的方式,而且为媒体行业升级和转型发展开辟了新的道路。播音主持工作者与AI主播之间的共生关系需要建立在彼此优势上,通过有效合作和互补,共同推动媒体行业发展。以播音主持“人机共生”实现策略作为研究对象,分析AI主播的优势及短板,探讨播音主持工作者的不可替代性,提出针对性的策略。

Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence:mechanisms and applications of cognition,management,and control for complex systems

In this paper,we aim to illustrate the concept of mutually trustworthy human-machine knowledge automation(HM-KA)as the technical mechanism of hybrid augmented intelligence(HAI)based complex system cognition,management,and control(CMC).We describe the historical development of complex system science and analyze the limitations of human intelligence and machine intelligence.The need for using human-machine HAI in complex systems is then explained in detail.The concept of“mutually trustworthy HM-KA”mechanism is proposed to tackle the CMC challenge,and its technical procedure and pathway are demonstrated using an example of corrective control in bulk power grid dispatch.It is expected that the proposed mutually trustworthy HM-KA concept can provide a novel and canonical mechanism and benefit real-world practices of complex system CMC.

Implementation of Strangely Behaving Intelligent Agents to Determine Human Intervention During Reinforcement Learning

Intrinsic motivation helps autonomous exploring agents traverse a larger portion of their environments.However,simulations of different learning environments in previous research show that after millions of timesteps of successful training,an intrinsically motivated agent may learn to act in ways unintended by the designer.This potential for unintended actions of autonomous exploring agents poses threats to the environment and humans if operated in the real world.We investigated this topic by using Unity’s MachineLearningAgent Toolkit(ML-Agents)implementation of the Proximal Policy Optimization(PPO)algorithm with the Intrinsic Curiosity Module(ICM)to train autonomous exploring agents in three learning environments.We demonstrate that ICM,although designed to assist agent navigation in environments with sparse reward generation,increasing gradually as a tool for purposely training misbehaving agent in significantly less than 1 million timesteps.We present the following achievements:1)experiments designed to cause agents to act undesirably,2)a metric for gauging how well an agent achieves its goal without collisions,and 3)validation of PPO best practices.Then,we used optimized methods to improve the agent’s performance and reduce collisions within the same environments.These achievements help further our understanding of the significance of monitoring training statistics during reinforcement learning for determining how humans can intervene to improve agent safety and performance.

“人-机-组织”共生系统:一个智能化组织理论框架

智能化组织作为人、机、组织多元共生时代的组织新形态,但其至今尚缺乏适配的组织理论框架,因而适时构建新型组织系统理论体系,已成引导企业数智化实践和深化理论创新的重要议题。基于人-机-环境系统工程理论和共生理论,创新性提出适配于企业数智化转型的人-机-组织共生系统理论,并从生成逻辑、基本内涵、运作机理、内容范畴与研究议程等方面系统探讨其理论框架体系。结果发现:人-机-组织共生系统作为探讨人、机、组织交互关系的整合理论框架,其形态呈现“人-组织二元系统→人-技术-组织三元交互系统→人-机-组织三元共生系统”的演化态势,且具有人机协同共生化、员工-组织合作共赢化、机器-组织交互赋能化、人-机-组织共生一体化等特征。同时,人-机-组织共生系统可划分为简单人-机-组织共生网络、复杂人-机-组织共生系统以及广义人-机-组织共生系统等3种基本类型,其共生模式包括互利共生、偏利共生、偏害共生、竞争吞噬4种类型。此外,人-机-组织共生系统理论的基本内容范畴主要涉及人类员工、智能机器、企业组织、员工-机器共生关系、员工-组织共生关系、机器-组织共生关系、人-机-组织三元共生关系7个方面。人-机-组织共生系统作为新生理论体系,既可为智能时代组织系统变革提供更富诠释力度的理论新视角,也可为引领组织系统数智化转型实践提供理论框架新范式。

人学视域下智能化教育变革的基础、价值及策略

人工智能的快速发展使智能机器成为现代生产的重要工具。在智能化发展中,人类需要划清人工智能与人类智能、机器思维与人类思维之间的界限。智能化教育变革以强调自我在智能化技术中的核心位置和人机共存为基础。人学视域下智能化教育变革的重要价值主要包括彰显智能化教育中人的主体性价值、突出教育在智能化中的必要性、重视劳动在智能化教育中的创造性地位三个方面。鉴于此,需要回归教育原点,重塑教育技术化理念,审视已有师生关系,重建智能化技术中的师生伦理,重视劳动在智能化教育中的价值,挖掘教育变革的社会生产动力,以强化人学视域下智能化教育变革成效。

数智融合环境下AIGC的场景化应用与发展机遇

[目的/意义]人工智能生成内容(AIGC)正推动着虚实融生趋势下内容创作的范式转化,对数智融合环境中AIGC应用场景和发展机遇的探索有助于促进AI技术的高效赋能和内容生态的价值实现。[研究设计/方法]着眼于AIGC的发展历程,从内容发展、概念类比和技术发展三个角度系统梳理AIGC的概念内涵。围绕生活场景、服务场景、文娱场景、科技场景和商业场景五大应用场景的自有特征,探析AIGC的交互形态和互动模式。[结论/发现] AIGC的技术基础是数字科技的更新迭代,建构理念是创作空间的扩容增益,但其本质特征还是内容生态的创新发展。整体来看AIGC的发展机遇主要体现在基于群体智能的内容共生、基于数智融合的能力升维和基于增量市场的价值共创三个层面。[创新/价值]随着底层技术和算法模型的突破性发展,AI催生的内容蓝海正在从辅助协作和降本提效向智能创作和增值创造扩散,为数智逻辑下的内容生态带来了全新的创作思路。

从交互壁垒到耦合共生:应急情报协同治理困境及纾困路径探赜——以重大突发公共卫生事件为例

[目的/意义]应急情报协同治理是一个实现应急情报从混乱到整齐,从无序到有序,总体向好发展为目标的动态、多维过程。[方法/过程]构建基于共生理论的重大突发公共卫生事件应急情报协同治理框架,以共生能量的生成隐喻协同能量的生成,对共生要素与协同治理要素进行相互印证,将共生要素的困境与疫情防控过程中应急情报协同治理的困境进行类比性植入和全景扫描,并基于质参量兼容原理、能量生成原理、系统进化原理、界面选择原理提出打破应急情报交互壁垒的纾困路径。[结果/结论]针对重大突发公共卫生事件应急情报协同治理的困境,提出基于共生要素的纾困路径:强化共生单元韧性,构建耦合联动的情报共同体;增拓共生环境容量,优化功能互补的协同治理工具箱;激发共生模式活力,打造双重共轭的情报保障链;优化共生界面选择,形塑多元平台和机制驱动的功能网。

人工智能赋能乡村教育振兴的限度及超越

人工智能赋能乡村教育振兴是优化乡村教育资源配置,提高乡村教育质量,实现乡村教育现代化和乡村教育振兴的现实选择。人工智能作为“手段”之利和乡村教育振兴的“目的”之需共同构成了人工智能与乡村教育融合的逻辑基础。同时,人工智能不是万能的也不是面面俱到的,在乡村教育振兴中存在着乡村教育精神空虚、乡村教育文化遮蔽、乡村教育特色丢失等方面的使用限度。应从智慧助推育人本质的回归,技术借力乡土文化的传承,智能推动异质发展的促进等方面加强乡村教育与人工智能的融合共生。

AI Safety Approach for Minimizing Collisions in Autonomous Navigation

Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions.These systems are developed under the term Artificial Intelligence(AI)safety.AI safety is essential to provide reliable service to consumers in various fields such asmilitary,education,healthcare,and automotive.This paper presents the design of an AI safety algorithmfor safe autonomous navigation using Reinforcement Learning(RL).Machine Learning Agents Toolkit(ML-Agents)was used to train the agentwith a proximal policy optimizer algorithmwith an intrinsic curiositymodule(PPO+ICM).This training aims to improve AI safety and minimize or prevent any mistakes that can cause dangerous collisions by the intelligent agent.Four experiments have been executed to validate the results of our research.The designed algorithmwas tested in a virtual environment with four differentmodels.A comparison was presented in four cases to identify the best-performing model for improvingAI safety.The designed algorithmenabled the intelligent agent to perform the required task safely using RL.A goal collision ratio of 64%was achieved,and the collision incidents were minimized from 134 to 52 in the virtual environment within 30min.

人机共生的复合脑:基于生成式人工智能辅助写作教学的应用发展及模式创新

生成式人工智能聊天机器人引发新技术的狂欢,加速了教育系统变革,写作领域首当其冲面临了巨大的挑战。新一代智能技术可以成为智能学伴,为学生提供个性化、交互式的学习支持服务,来帮助学生高效地进行自主学习。如何利用智能技术增强学生写作学习的效果和效益也成为迫切需要解决的问题。在梳理技术辅助写作教与学的发展历程基础上,立足当前生成式人工智能高速发展的浪潮,廓清写作内涵和深层学理机制,构建人机共生的写作模式,可为写作提升以及高阶思维的培养提供参考路径。人机共生的写作模式将写作定义为生成式人工智能外脑与人类内脑创生融合的过程,该过程借助互动反馈,促进人工智能外脑与人类内脑的个性化演进成长,进而不断提升写作效率和质量。同时,为应对生成式人工智能的局限和潜在风险,在人机共生写作中需要帮助学生具备高质量提问和指令的能力,避免其过度依赖和沉迷人工智能,同时,学校和教师要积极变革教学模式,推广项目化学习和大概念单元整体写作教学,发展真实性写作,提升学生的核心素养。

元宇宙视域下的智慧图书馆应用场景开发与服务实践

新技术环境下,图书馆加快应用元宇宙技术,探索智慧图书馆沉浸式服务,为图书馆智慧化发展提供新的方向和实践场域。文章梳理元宇宙核心技术和智慧图书馆不同发展阶段的技术基础,探讨二者之间的契合点,据此提出融合路径。围绕智慧图书馆设备、技术和职能等核心要素,结合元宇宙关键技术、内容创作和生态系统,设计元宇宙智慧图书馆技术架构和功能,构建虚实共生的AI+3D+VR元宇宙智慧图书馆场景和服务模型。最后,基于元宇宙智能互动引擎,探讨元宇宙智慧图书馆沉浸式教育和学习空间场景技术开发,体验知识服务新模式。