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

Coordinated lane change in autonomous driving: a computationally aware solution

We consider the problem of optimal charging of plug-in electric vehicles (PEVs). We treat this problem as a multi-agent game, where each vehicle/agent is subject to possibly different constraints. Under this set-up, we show that, for any finite number of possibly heterogeneous agents, the PEV charging control game admits a unique Nash equilibrium, which is the optimizer of an auxiliary minimization program. To characterize the population of heterogeneous PEVs as its size grows to infinity, we assume that the parameters defining the constraints of each vehicle are drawn randomly from a given distribution. We are then able to show that, as the number of agents tends to infinity, the value of the game and the social optimum of the cooperative counterpart of the problem under study coincide for almost any choice of the random heterogeneity parameters. Moreover, in the case of a discrete probability distribution, we provide a systematic way to abstract agents in homogeneous groups and show that the effect of heterogeneity averages out as their number tends to infinity. We also show that, for any finite number of agents, the desired Nash equilibrium can be computed exactly by means of a regularized Jacobi algorithm, and support our theoretical results via a detailed simulation study.

Nash equilibria in electric vehicle charging control games: Decentralized computation and connection with social optima

We study distributed optimization in a cooperative multi-agent setting, where agents need to agree on the usage of shared resources and can communicate via a time-varying network to this purpose. Each agent has its own decision variables that should be set so as to minimize its individual objective function subject to local constraints. Resource sharing is modeled via coupling constraints that involve the non-negativity of the sum of agents' individual functions, each one depending on the decision variables of one single agent. We propose a novel distributed algorithm to minimize the sum of the agents' objective functions subject to both local and coupling constraints, where dual decomposition and proximal minimization are combined in an iterative scheme. Notably, privacy of information is guaranteed since only the dual optimization variables associated with the coupling constraints are exchanged by the agents. Under convexity assumptions, jointly with suitable connectivity properties of the communication network, we are able to prove that agents reach consensus to some optimal solution of the centralized dual problem counterpart, while primal variables converge to the set of optimizers of the centralized primal problem. A numerical example shows the efficacy of the proposed approach.

Dual decomposition and proximal minimization for multi-agent distributed optimization with coupling constraints

The problem of semantic indexing of audio-visual documents is of great interest due to the wide diffusion of large audio-video databases. In this paper, we propose a semantic indexing algorithm based on the controlled Markov chain modelling framework. Controlled Markov chain models are used to describe the temporal evolution of low-level descriptors extracted from the MPEG compressed bit-stream. The proposed algorithm has been conceived for soccer game video sequences, and seems promising based on the simulation results obtained in this preliminary study.

Modelling of visual features by Markov chains for sport content characterization

We investigate the connections between compression learning and scenario based optimization. We consider different constrained optimization problems affected by uncertainty represented by means of scenarios and show that the issue of providing guarantees on the probability of constraint violation reduces to a learning problem for an appropriately chosen algorithm that enjoys compression learning properties. The compression learning perspective provides a unifying framework for scenario based optimization and allows us to revisit the scenario approach and the probabilistically robust design, a recently developed technique based on a mixture of randomized and robust optimization. Our analysis shows that all optimization problems we consider here, even though they are of different type, share certain similarities, which translates on similar feasibility properties of their solutions.

Maria Prandini

Papers

Coordinated lane change in autonomous driving: a computationally aware solution

Nash equilibria in electric vehicle charging control games: Decentralized computation and connection with social optima

Dual decomposition and proximal minimization for multi-agent distributed optimization with coupling constraints

Modelling of visual features by Markov chains for sport content characterization

A compression learning perspective to scenario based optimization