A smart machine is a machine that is embedded with cognitive computing ability, which uses artificial intelligence and machine learning algorithms to sense, learn, reason, and interact with people in different ways. A smart machine can solve a problem accurately and precisely, and provide the desired output. Both the Manufacturing as well as the Service sectors are the major end-users of smart machines. The need to provide quality products and services has led to the adoption of smart machines across sectors.
This chapter introduces the Internet of Intelligent Things (IoIT), the future Internet of Things (IoT) with significant intelligence added to "things". We discuss the importance of Artificial Intelligence approaches to enable such Intelligent Communication Networks. Nowadays, sensor networks are becoming a reality, especially for remote monitoring of events in fields such as healthcare, military, forest integrity or prediction of seismic activity in volcanoes. Intelligent devices and sensors are also appearing, besides electronic home appliances and utilities, as gadgets to mobile phones or tablets. And some of these devices have capability to actuate on the world. This chapter is focused on surveying current approaches for the Internet of all these intelligent things connected and communicating. It addresses artificial intelligence techniques employed to create such intelligence, and network solutions to exploit the benefits brought by this capability.
Contextual awareness refers to having autonomous robots reason about their capabilities and limitation and improve on those limitations through the help of others. This project explored three areas of contextual awareness detecting when anomalous behaviors occur, reacting to situations where plans are failing, and learning new plans through human demonstration. Each of these areas is important in achieving robust, reliable robot autonomy. The work on detecting anomalous behavior focused on finding subtle anomalies that could not be detected from single events; the work on reacting to failing plans focused on deciding when to switch between risk-neutral and risk-seeking policies, for domains in which the goal is to achieve above a certain threshold of reward; and the work on learning new plans focused on complex manipulator trajectories, where multiple human examples are combined so as to smooth out noise in the examples without losing important details. The first and third areas were demonstrated using actual robots; the second area was demonstrated using a video game simulator.
Current trends in precision machinery include increased adaptability,speed and reliability.This,combined with the development of artificially-intelligent automatic sensors can lead to the establishment of highly-reliable and systematic manufacturing systems.During the automation process,equipment process parameters frequently need to be adjusted to match the requirements of different processes.Thus how to best maintain normal equipment operation and stable quality through these frequent adjustments is a key issue for manufacturers.Therefore,high-quality automated production systems allowing for fast-changeover and real-time automatic detection and performance monitoring are effectively needed.
在过渡规划问题(over-subscribed planning,简称 OSP)研究中,如果目标之间不是相互独立的,那么目标坚定效益依赖比单个目标效益更能提高规划解的质量.但是,已有的描述模型不符合标准规划描述语言(planning domain descrion language,简称PDDL)的语法规范,不能在一般的OSP规划系统上进行推广,提出了用派生谓词规则和目标偏好描述效益依赖的方法,这二者均为ＰDDL语言的基本要素.实质上,将已有的GAI模型转化为派生谓词规则和目标偏好,其中派生谓词规则显式描述目标子集的存在条件,偏好机制用来表示目标子集的效益,二者缺一不可.该转换算法既可以保持在描述依赖关系时GAI模型的易用性和直观性上,又可以扩展一般的OSP规划系统处理目标效益依赖的能力.从理论上可以证明该算法在转化过程中的语义不变性,子啊基准领域的实验结果表明其可行性和规划解质量的改善能力.提出符合PDDL语言规范的目标效益依赖关系的描述形式,克服了已有模型不通用的缺点.
The SWARMS project brings together experts in artificial intelligence, control theory, robotics, systems engineering and biology with the goal of understanding swarming behaviors in nature and applications of biologically-inspired models of swarm behaviors to large networked groups of autonomous vehicles. The main goal is to develop a framework and methodology for the analysis of swarming behavior in biology and the synthesis of bio-inspired swarming behaviors for engineered systems. We are interested in such questions as: Can large numbers of autonomously functioning vehicles be reliably deployed in the form of a 'swarm' to carry out a prescribed mission and to respond as a group to high-level management commands. Can such a group successfully function without a designated leader, with limited communications between its members, and with dynamically changing 'role', for its members. Is there a hierarchy of 'compatibls' models appropriate to swarming/schooling/flocking which is rich enough to explain these behaviors at various 'resolution' ranging from aggregate characterizations of emergent behavior to detailed descriptions which model individual vehicle dynamics.
关键词：矿井;救生舱;井下空间环境;人工智能仿真;Mine;Mobile refuge;Underground space environment;Artificial intelligence simulation