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Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
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
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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
Using the DiaSpec design language and compiler to develop robotics systems
A Sense/Compute/Control (SCC) application is one that interacts with the
physical environment. Such applications are pervasive in domains such as
building automation, assisted living, and autonomic computing. Developing an
SCC application is complex because: (1) the implementation must address both
the interaction with the environment and the application logic; (2) any
evolution in the environment must be reflected in the implementation of the
application; (3) correctness is essential, as effects on the physical
environment can have irreversible consequences. The SCC architectural pattern
and the DiaSpec domain-specific design language propose a framework to guide
the design of such applications. From a design description in DiaSpec, the
DiaSpec compiler is capable of generating a programming framework that guides
the developer in implementing the design and that provides runtime support. In
this paper, we report on an experiment using DiaSpec (both the design language
and compiler) to develop a standard robotics application. We discuss the
benefits and problems of using DiaSpec in a robotics setting and present some
changes that would make DiaSpec a better framework in this setting.Comment: DSLRob'11: Domain-Specific Languages and models for ROBotic systems
(2011
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Adaptive object-modeling : patterns, tools and applications
Tese de Programa Doutoral. Informática. Universidade do Porto. Faculdade de Engenharia. 201
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