Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, time-delays, and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided. Our analysis framework is based on tools from matrix theory, algebraic graph theory, and control theory. We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in small-world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms. A brief introduction is provided on networked systems with nonlocal information flow that are considerably faster than distributed systems with lattice-type nearest neighbor interactions. Simulation results are presented that demonstrate the role of small-world effects on the speed of consensus algorithms and cooperative control of multivehicle formations

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Consensus and Cooperation in Networked Multi-Agent Systems

Data Mining Practical Machine Learning Tools and Techniques

This paper reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

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An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination

System Identification I

A set-based approach to model checking of nonlinear systems (invited tutorial)

This paper deals with the problem of recovering the input signal applied to a linear time-invariant system from the measures of its output and the a-priori knowledge of the input statistics (blind equalization). Under the assumption of an i.i.d. non-Gaussian input sequence, a new iterative procedure based on phase sensitive high-order cumulants for adjusting the coefficients of a transversal equalizer is introduced. The main feature of the proposed technique is that it realizes the automatic selection of the equalization delay so as to improve the equalization performance.

A new algorithm for the automatic search of the best delay in blind equalization

Performance analysis of a digital compass for the heading estimation in nautical application

In this paper, we address power control in a wireless cellular network where multiple mobile users are served by a set of base stations, one per cell. Their reference base station has then to set their transmission power so as to maximize the level of service measured in terms of transmission throughput. Interference due to communication of devices on the same wireless channel is coupling the decisions of all base stations in the cellular network. We propose a distributed algorithm based on proximal minimization that makes the base stations reach consensus to a solution that guarantees an optimal throughput for all mobile devices, by appropriately setting the signal to interference plus noise ratio. The introduced algorithm is compared with a gradient-based distributed algorithm via an extensive simulation study, which reveals the advantage of proximal minimization in the case when the cost function is non-differentiable and the sub-gradient has to be computed.

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A proximal minimization based distributed approach to power control in wireless networks: Performance and comparative analysis

We consider a multi-agent system where each agent has its own estimate of a given quantity and the goal is to reach consensus on the average. To this purpose, we propose a distributed consensus algorithm that guarantees convergence to the average in a finite number of iterations. The algorithm is tailored to ring networks with bidirectional pairwise communications. If the number of agents $m$ is even, say $m=2n$, then, the number of iterations needed is equal to $n$, which in this case is the diameter of the network, whereas the number of iterations grows to $3n$ if the number of agents is odd and equal to $m=2n+1$.

Maria Prandini

Papers

A set-based approach to model checking of nonlinear systems (invited tutorial)

A new algorithm for the automatic search of the best delay in blind equalization

Performance analysis of a digital compass for the heading estimation in nautical application

A proximal minimization based distributed approach to power control in wireless networks: Performance and comparative analysis

Finite time distributed averaging over ring networks