6,166 research outputs found
Quantifying the Evolutionary Self Structuring of Embodied Cognitive Networks
We outline a possible theoretical framework for the quantitative modeling of
networked embodied cognitive systems. We notice that: 1) information self
structuring through sensory-motor coordination does not deterministically occur
in Rn vector space, a generic multivariable space, but in SE(3), the group
structure of the possible motions of a body in space; 2) it happens in a
stochastic open ended environment. These observations may simplify, at the
price of a certain abstraction, the modeling and the design of self
organization processes based on the maximization of some informational
measures, such as mutual information. Furthermore, by providing closed form or
computationally lighter algorithms, it may significantly reduce the
computational burden of their implementation. We propose a modeling framework
which aims to give new tools for the design of networks of new artificial self
organizing, embodied and intelligent agents and the reverse engineering of
natural ones. At this point, it represents much a theoretical conjecture and it
has still to be experimentally verified whether this model will be useful in
practice.
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
Fast Optimization with Zeroth-Order Feedback in Distributed, Multi-User MIMO Systems
In this paper, we develop a gradient-free optimization methodology for
efficient resource allocation in Gaussian MIMO multiple access channels. Our
approach combines two main ingredients: (i) an entropic semidefinite
optimization based on matrix exponential learning (MXL); and (ii) a one-shot
gradient estimator which achieves low variance through the reuse of past
information. This novel algorithm, which we call gradient-free MXL algorithm
with callbacks (MXL0), retains the convergence speed of gradient-based
methods while requiring minimal feedback per iterationa single scalar. In
more detail, in a MIMO multiple access channel with users and transmit
antennas per user, the MXL0 algorithm achieves -optimality
within iterations (on average and with high
probability), even when implemented in a fully distributed, asynchronous
manner. For cross-validation, we also perform a series of numerical experiments
in medium- to large-scale MIMO networks under realistic channel conditions.
Throughout our experiments, the performance of MXL0 matchesand
sometimes exceedsthat of gradient-based MXL methods, all the while operating
with a vastly reduced communication overhead. In view of these findings, the
MXL0 algorithm appears to be uniquely suited for distributed massive MIMO
systems where gradient calculations can become prohibitively expensive.Comment: Final version; to appear in IEEE Transactions on Signal Processing;
16 pages, 4 figure
Error threshold in optimal coding, numerical criteria and classes of universalities for complexity
The free energy of the Random Energy Model at the transition point between
ferromagnetic and spin glass phases is calculated. At this point, equivalent to
the decoding error threshold in optimal codes, free energy has finite size
corrections proportional to the square root of the number of degrees. The
response of the magnetization to the ferromagnetic couplings is maximal at the
values of magnetization equal to half. We give several criteria of complexity
and define different universality classes. According to our classification, at
the lowest class of complexity are random graph, Markov Models and Hidden
Markov Models. At the next level is Sherrington-Kirkpatrick spin glass,
connected with neuron-network models. On a higher level are critical theories,
spin glass phase of Random Energy Model, percolation, self organized
criticality (SOC). The top level class involves HOT design, error threshold in
optimal coding, language, and, maybe, financial market. Alive systems are also
related with the last class. A concept of anti-resonance is suggested for the
complex systems.Comment: 17 page
Probabilistic Interpretation of Linear Solvers
This manuscript proposes a probabilistic framework for algorithms that
iteratively solve unconstrained linear problems with positive definite
for . The goal is to replace the point estimates returned by existing
methods with a Gaussian posterior belief over the elements of the inverse of
, which can be used to estimate errors. Recent probabilistic interpretations
of the secant family of quasi-Newton optimization algorithms are extended.
Combined with properties of the conjugate gradient algorithm, this leads to
uncertainty-calibrated methods with very limited cost overhead over conjugate
gradients, a self-contained novel interpretation of the quasi-Newton and
conjugate gradient algorithms, and a foundation for new nonlinear optimization
methods.Comment: final version, in press at SIAM J Optimizatio
Word-of-mouth interaction and the organization of behaviour
We present a discrete choice model based on agent interaction. The framework combines the features of two well-known models of word-of-mouthcommunication (Ellison and Fudenberg, 1995 and Bala and Goyal, 2001).Interaction structure is a regular periodic lattice with decision-makers interacting only with immediate neighbours. We investigate the long-runequilibrium) behaviour of the resulting system and show that for a largerange of initial conditions clustering in economic behaviour emerges andpersists inde?nitely. The setup allows for the analysis of multi-option environments. For these environments we derive the distribution of optionpopularity in equilibrium.word-of-mouth, inertia, clustering, choice.
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