Ontology acquisition and exchange of evolutionary product-brokering agents

Agent-based electronic commerce (e-commerce) has been booming with the development of the Internet and agent technologies. However, little effort has been devoted to exploring the learning and evolving capabilities of software agents. This paper addresses issues of evolving software agents in e-commerce applications. An agent structure with evolution features is proposed with a focus on internal hierarchical knowledge. We argue that knowledge base of agents should be the cornerstone for their evolution capabilities, and agents can enhance their knowledge bases by exchanging knowledge with other agents. In this paper, product ontology is chosen as an instance of knowledge base. We propose a new approach to facilitate ontology exchange among e-commerce agents. The ontology exchange model and its formalities are elaborated. Product-brokering agents have been designed and implemented, which accomplish the ontology exchange process from request to integration

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

Customer-engineer relationship management for converged ICT service companies

Thanks to the advent of converged communications services (often referred to as ‘triple play’), the next generation Service Engineer will need radically different skills, processes and tools from today’s counterpart. Why? in order to meet the challenges of installing and maintaining services based on multi-vendor software and hardware components in an IP-based network environment. The converged services environment is likely to be ‘smart’ and support flexible and dynamic interoperability between appliances and computing devices. These radical changes in the working environment will inevitably force managers to rethink the role of Service Engineers in relation to customer relationship management. This paper aims to identify requirements for an information system to support converged communications service engineers with regard to customer-engineer relationship management. Furthermore, an architecture for such a system is proposed and how it meets these requirements is discussed

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Improving Collaborative Learning Using Pervasive Embedded System-Based Multi-Agent Information and Retrieval Framework in Educational Systems

E-learning is a form of Technology SupportedEducation where the medium of instruction is throughDigital Technologies, particularly Computer Technology.An instance is the use of search engines like Google andYahoo, which aid Collaborative Learning. However, thewidespread provision of distributed, semi-structuredinformation resources such as the Web has obviouslybrought a lot of benefits; but it also has a number ofdifficulties. These difficulties include people gettingoverwhelmed by the sheer amount of information available,making it hard for them to filter out the junk andirrelevancies and focus on what is important, and also toactively search for the right information. Also, people easilyget bored or confused while browsing the Web because ofthe hypertext nature of the web, while making it easy to linkrelated documents together, it can also be disorienting. Toalleviate these problems, the Web Information Food ChainModel was introduced. How effective has this been with thedynamic nature of computing technologies? Pervasivecomputing devices enable people to gain immediate accessto information and services anywhere, anytime, withouthaving to carry around heavy and impractical computingdevices. Thus, the bulky PCs become less attractive andbeing slowly eroded with the development of a newgeneration of smart devices like wireless PDAs, smartphones, etc. These embedded devices are characterized bybeing unobtrusively embedded; completely connected;intuitively intelligent; effortlessly portable and mobile; andconstantly on and available. This paper presents the use ofembedded systems and Intelligent Agent-Based WebInformation Food Chain Model in Multi-Agent Informationand Retrieval Framework (IIFCEMAF), to realizing fullpotentials of the internet, for users’ improved system ofcollaborative e-learning in education

Cooperative knowledge processing: the key technology for future organizations

Drawing from the challenges organizations are faced with today, there is a growing understanding that future market success, and long-term survival of enterprises will increasingly be related to the effectiveness of information technology utilization. This, however, requires to intertwine much more seriously organizational theory and research in information processing as it has been done before. Within this paper, we approached this aim from the perspective of radically decentralized, computerized enterprises. We further assume that organizations are increasingly processoriented, rather than applying to structuring organizations based on task decomposition and assignment. This scenario reveals that, due to the inherent autonomy of organizational units, the coordination of decentralized organizational activities (workflows, processes) necessitates a cooperative style of problem solving. On this basis, the paper introduces into the research area of cooperative knowledge processing, with a particular focus on multi-agent decision support systems, and human computer cooperative work. Finally, several important organizational applications of cooperative knowledge processing are presented that demonstrate how future enterprises can take great advantage from these new technologies.<br

A Multi-Agent Architecture for An Intelligent Web-Based Educational System

An intelligent educational system must constitute an adaptive system built on multi-agent system architecture. The multi-agent architecture component provides self-organization, self-direction, and other control functionalities that are crucially important for an educational system. On the other hand, the adaptiveness of the system is necessary to provide customization, diversification, and interactional functionalities. Therefore, an educational system architecture that integrates multi-agent functionality [50] with adaptiveness can offer the learner the required independent learning experience. An educational system architecture is a complex structure with an intricate hierarchal organization where the functional components of the system undergo sophisticated and unpredictable internal interactions to perform its function. Hence, the system architecture must constitute adaptive and autonomous agents differentiated according to their functions, called multi-agent systems (MASs). The research paper proposes an adaptive hierarchal multi-agent educational system (AHMAES) [51] as an alternative to the traditional education delivery method. The document explains the various architectural characteristics of an adaptive multi-agent educational system and critically analyzes the system’s factors for software quality attributes