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Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic.
Artificial intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Machine learning
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Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory
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Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience.
Graphical modeling (Statistics) --- Multivariate analysis --- Graphic methods --- Machine learning
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Wireless Sensor Networks: Theory and Practice for Deployment addresses WSNs deployment, a mandatory and critical step in the process of developing WSNs solutions for real-life applications. The authors address simple approaches to deploy static WSNs, then exploring moreïÅưsophisticated approaches to deploy mobile WSNs. Featuring detailed investigations of deployment-related issues such as deployment cost, coverage, connectivity, sensors reliability, and harsh deployment environments, this book will equip you with the basics and an advanced view of both the theoretical and practical aspects, along with knowledge of theïÅưguidelines for WSNs deployment.
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The analysis of QoE is not an easy task, especially for multimedia services, because all the factors (technical and non-technical) that directly or indirectly influence the user-perceived quality have to be considered. This book describes different methods to investigate users' QoE from the viewpoint of technical and non-technical parameters using multimedia services. It discusses the subjective methods for both controlled and uncontrolled environments. Collected datasets are used to analyze users' profiles, which sheds light on key factors to help network service providers understand end-users' behavior and expectations. Important adaptive video streaming technologies are discussed that run on unmanaged networks to achieve certain QoS features. The authors present a scheduling method to allocate resources to the end-user based on users' QoE and optimizes the power efficiency of users' device for LTE-A. Lastly, two key aspects of 5G networks are presented: QoE using multimedia services (VoIP and video), and power-saving model for mobile device and virtual base station.
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Addressing the QoE for improving customer perception when using Added Value Services offered by service providers, from the evaluation to the monitoring and other management processes. The main goal of the book is to present state-of-the-art research results and experience reports in the area of Quality Monitoring for Customer experience management, addressing amongst others currently important topics such as Service-aware Future Internet architecture for Quality of Experience (QoE) management on multimedia applications.
Customer services --- Customer services --- Quality control --- Total quality management --- Management. --- Quality control. --- Management. --- Data processing.
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This book reviews the concept of Software-Defined Networking (SDN) by studying the SDN architecture. It provides a detailed analysis of state-of-the-art distributed SDN controller platforms by assessing their advantages and drawbacks and classifying them in novel ways according to various criteria. Additionally, a thorough examination of the major challenges of existing distributed SDN controllers is provided along with insights into emerging and future trends in that area. Decentralization challenges in large-scale networks are tackled using three novel approaches, applied to the SDN control plane presented in the book. The first approach addresses the SDN controller placement optimization problem in large-scale IoT-like networks by proposing novel scalability and reliability aware controller placement strategies. The second and third approaches tackle the knowledge sharing problem between the distributed controllers by suggesting adaptive multilevel consistency models following the concept of continuous Quorum-based consistency. These approaches have been validated using different SDN applications, developed from real-world SDN controllers.
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