31,825 research outputs found

    A Qualitative Analysis of Online Gaming:

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    The popularity of Massively Multi-Player Online Role-Playing Games (MMORPGs) has risen dramatically over the last decade. Some gamers spend many hours a day in these virtual environments interacting with others gamers, completing quests, and forming social groups. The present study set out to explore the experiences and feelings of online gamers. The study comprised 71 interviews with online gamers (52 males and 19 females) from 11 different countries. Many themes emerged from the analyses of the interview transcripts including (i) relationship with social networking, (ii) social interaction, (iii) the community, (iv) learning real-life skills, (v) reinforcement schedules and operant conditioning, (vi) game design and content, (vii) escaping from real life, (viii) playing longer than intended, and (ix) gamers’ obligations towards others in online worlds. These findings specifically showed the social networking capabilities of online gaming, the community aspects and the psychological mechanisms within MMORPGs that can lead to excessive online gaming. The implications of these findings are discussed in relation to previous qualitative and quantitative research in the area

    Deep Reinforcement Learning for Resource Management in Network Slicing

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    Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201

    Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

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    IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
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