On geometric and topological reasoning in robotics

The interplay of AI and symbolic mathematical computation and traditional mathematics is discussed here on the basis of some selected aspects from geometric, topological and logical reasoning in the classical AI field of robotics. The kinematics model of a robot arm is considered in detail and methods from symbolic computation are applied to inverse kinematics and singularity problems. It is shown how methods from classical geometry and topology can give support to work on typical robotics questions like the existence of singular configurations. Finally a novel approach for logical modeling in robotics is sketched based on so-called logical fiberings. This revised version was published online in June 2006 with corrections to the Cover Date.     

Artificial intelligence: New jobs from old

The age of artificial intelligence (AI) is upon us, and its effect upon society in the coming years will be noteworthy. Artificial intelligence is a field that encompasses such applications as robotics, expert systems, natural language understanding, speech recognition, and computer vision. The effect of these AI systems upon existing and future job occupations will be important. This paper takes a look at artificial intelligence in terms of the creation of new job categories. Also, the introduction of AI into the organization to better familiarize the employees about AI will be discussed.     

Using neural networks to predict financial reporting fraud: Part 1

Artificial Intelligence (AI) is the science of building computer devices and software applications that mimic many of the characteristics that we associate with human behaviour, such as the ability to reason, see, learn, solve problems, understand languages, and so on. AI systems include natural languages, robotics, expert systems and neural networks. This article focuses on neural networks, which can be designed for problems that arise at any managerial level and may be used to predict the occurrence of fraud.     

Using neural networks to predict financial reporting fraud: Part 2

Artificial Intelligence (AI) is the science of building computer devices and software applications that mimic many of the characteristics that we associate with human behaviour, such as the ability to reason, see, learn, solve problems, understand language and so on. AI systems include natural languages, robotics, expert systems and neural networks. This article focuses on neural networks, which can be designed for problems that arise at any managerial level and may be used to predict the occurrence of fraud.     

What do we owe to intelligent robots?

AI & SOCIETY - Great technological advances in such areas as computer science, artificial intelligence, and robotics have brought the advent of artificially intelligent robots within our reach...     

Expert and Non-expert Opinion About Technological Unemployment

There is significant concern that technological advances, especially in robotics and artificial intelligence (AI), could lead to high levels of unemployment in the coming decades. Studies have estimated that around half of all current jobs are at risk of automation. To look into this issue in more depth, we surveyed experts in robotics and AI about the risk, and compared their views with those of non-experts. Whilst the experts predicted a significant number of occupations were at risk of automation in the next two decades, they were more cautious than people outside the field in predicting occupations at risk. Their predictions were consistent with their estimates for when computers might be expected to reach human level performance across a wide range of skills. These estimates were typically decades later than those of the non-experts. Technological barriers may therefore provide society with more time to prepare for an automated future than the public fear. In addition, public expectations may need to be dampened about the speed of progress to be expected in robotics and AI.     

The Fourth Industrial Revolution and implications for innovative cluster policies

The Fourth Industrial Revolution has become a global buzz word since the World Economic Forum (WEF) adopted it as an annual issue in 2016. It is represented by hyper automation and hyper connectivity based on artificial intelligence (AI), big data, robotics, and Internet of things (IoT). AI, big data, and robotics can contribute to developing hyper automation that can increase productivity and intensify industrial production. Particularly, robots using AI can make decision by themselves as human being on complicated processes. Along with the hyper automation, the hyper connectivity increases not only at national, but also global level by using information and communication technologies (ICT). IoT is the core technology to create the hyper connectivity in Cyber Physical System (CPS) that connects technology, nature, and human being. Accordingly, a perfect convergence between ICT and manufacturing can be completed in the Fourth Industrial Revolution era and an extremely efficient flexible production system by spreading IoT in CPS will be established. Under such a condition, innovative clusters must play their traditional roles in cradles of technology innovation and commercialization. It must be difficult challenges for innovative clusters to meet their targets and to be adjusted by the changing new environment at the same time. This paper argues how the Fourth Industrial Revolution can change the global production chain and how core technologies function in industries. Furthermore, it focuses on how innovative clusters have to evolve to respond the Fourth Industrial Revolution. Last, but not least it also analyzes whether or not innovative clusters can play their roles as technology innovation hubs in the real world and CPS in the Fourth Industrial Revolution era.     

Vision-based robotics: a challenge to real world Artificial Intelligence

Vision is a key function not only for robotics but also for AI more generally. Today real-time visual processing is becoming possible; this means that vision based behavior can become more dynamic, opening fertile areas for applications. One aspect of this is real-time visual tracking. We have built a real-time tracking system and incorporated it in an integrated robot programming environment. Using this, we have performed experiments in vision based robot behavior and human-robot interactions. In particular, we have developed a robotic system capable of 'learning by seeing'. In general, it is important for the AI community not to lose sight of the problems and progress of robotics. After all, an AI system which acts in real-time in the real-world is no less (and no more) than an intelligent robot.     

Reinforcement learning in robotic applications: a comprehensive survey

In recent trends, artificial intelligence (AI) is used for the creation of complex automated control systems. Still, researchers are trying to make a completely autonomous system that resembles human beings. Researchers working in AI think that there is a strong connection present between the learning pattern of human and AI. They have analyzed that machine learning (ML) algorithms can effectively make self-learning systems. ML algorithms are a sub-field of AI in which reinforcement learning (RL) is the only available methodology that resembles the learning mechanism of the human brain. Therefore, RL must take a key role in the creation of autonomous robotic systems. In recent years, RL has been applied on many platforms of the robotic systems like an air-based, under-water, land-based, etc., and got a lot of success in solving complex tasks. In this paper, a brief overview of the application of reinforcement algorithms in robotic science is presented. This survey offered a comprehensive review based on segments as (1) development of RL (2) types of RL algorithm like; Actor-Critic, DeepRL, multi-agent RL and Human-centered algorithm (3) various applications of RL in robotics based on their usage platforms such as land-based, water-based and air-based, (4) RL algorithms/mechanism used in robotic applications. Finally, an open discussion is provided that potentially raises a range of future research directions in robotics. The objective of this survey is to present a guidance point for future research in a more meaningful direction.

     

Measuring learning progress in intelligent autonomous robots

AI researchers claim to understand some aspect of human intelligence when their model is able to “emulate” it. In the contexts of mobile robots, the ability to go from teleoperation to autonomy should accordingly be treated not simply as a trick for robotics hackers but as an important epistemological and methodological goal. In this article the experience derived from designing locomotion control systems for both teleoperated and autonomous multi-legged articulated robots is described.     

多智能体团队合作在机器人足球赛中的应用

Robocup机器人足球比赛是近年来人工智能和机器人学迅速发展的一个重要的研究领域,通过这个平台,可以来评价各种理论和算法。但由于机器人足球比赛系统固有的动态性、不确定性及实时性,这就要求整个智能体团队的合作结构能够应付这种复杂环境。针对这点,文中探讨了一种基于阵型和角色的方法来实现多智能体的团队合作,通过应用到客户端程序上所取得的良好效果,证明此方法对于多智能体的团队合作是有效的。     

Collective intelligence: it's all in the numbers

AI has been an exporter of ideas to computing in general (neural networks, agents, though robotics is more complex). But AI is now embracing ideas from elsewhere that were initially scorned because they were thought to have nothing to do with modeling intelligence and, especially, human intelligence. These are the statistical and probabilistic approaches to information capture and use that have become particularly prominent in machine learning but have spread all over AI in the last two decades. Pattern recognition was accepted in particular areas, like machine vision, as a kind of technological fix. But statistical and probabilistic approaches are now mainstream.     

增强现实系统中基于视觉感知的抓取识别

增强现实系统采用单目光学透视式头盔时必然会使虚拟物体呈现在真实物体之前,使用户无法得知应该在何处抓取虚拟物体,并且系统所得到的抓取数据也会受硬件本身、人和环境等因素影响而产生偏差.从人的视觉感知习惯入手,利用机器人学和光线几何理论实现基于视觉感知的碰撞检测;通过分析抓取过程,得出3条抓取知识规则,并将其与模糊理论结合应用于证据推理.实验结果表明,采用文中方法,用户能轻松地实现抓取,整个交互过程相当自然.     

人工智能技术在机器人运动规划中的应用

机器人的运动规划问题是机器人学和人工智能中的一个重要课题。总结了国内外在机器人运动规划领域的一些典型算法，讨论了各种人工智能技术在运动规划中的应用，介绍了在空间机器人运动规划课题研究中的一些成果。     

Software engineering for robotics [From the Guest Editors]

This special issue on software engineering (SE) for robotics captures a snapshot of current research issues and state-of-the-practice in robotic software development, a topic that has recently received increasing attention from the robotics community, thanks to some popular initiatives such as the Microsoft move into robotics.     

Intelligent Manufacturing:Present State and Future Trends

AI methods are introduced to an increasing extent in the field of CIM and robotics. This development results in intelligent CIM components – ICAD, ICAP, ICAM, ICAQ – and in intelligent manufacturing systems (IMS) as well as in intelligent robots. This new philosophy requires a lot of prerequisites and research. ICIM or IMS is partially introduced in industry but mainly for large companies. AI as knowledge based and expert systems is ready to be introduced in an efficient way in CIM components. Intelligent, mobile robots as integrated parts of CIM and IMS are ready to be used in factory automation. One of the next steps could be the introduction of Multi-Agent and/or holonic systems. In this contribution the history and the present state of flexible manufacturing, with special emphasis on robotics, are described and further trends in development – Multi Agent Systems – are discussed mainly from the viewpoint of SMEs.     

Moving AI out of its infancy: changing our preconceptions

Explicit symbolic logic has faded from prominence, but the close coupling of AI and the digital computer, and of thought and the stepwise algorithm, seem about as strong and unquestioned as ever. Of course there's connectionism, but this too is mired in false assumptions that date back a long way. And it seems to have dragged neuroscience down with it to the extent that we now seem unable to think about real brains without resorting to models that owe too much of their inspiration to the three-layer perceptron. Traditional AI has excelled at solving certain kinds of problems. It can make systems that learn but not in any generally applicable way. AI is about making machines do what humans use intelligence to do, and often this doesn't actually require the machines to show any intelligence at all. But for many tasks, especially in robotics, the ability to see, learn, and perform complex motor actions is a prerequisite that the traditional approach has utterly failed to fulfill.     

RoboCup小型组足球机器人主控系统的设计

RoboCup作为在世界上最有影响力的赛事之一受到世界各地机器人爱好者的推崇,该文对其中极具代表性的小型组足球机器人(F180)进行了研究,并提出了F180主控系统的设计方案,内容包括子系统模块的划分、视觉处理子系统的设计方案、智能策略子系统的设计方案等。     

RoboCup小型组足球机器人主控系统的设计

RoboCup作为在世界上最有影响力的赛事之一受到世界各地机器人爱好者的推崇,该文对其中极具代表性的小型纽足球机器人（F180）进行了研究,并提出了F180主控系统的设计方案,内容包括子系统模块的划分、视觉处理子系统的设计方案、智能策略子系统的设计方案等。