97,812 research outputs found
Mean-Field-Type Games in Engineering
A mean-field-type game is a game in which the instantaneous payoffs and/or
the state dynamics functions involve not only the state and the action profile
but also the joint distributions of state-action pairs. This article presents
some engineering applications of mean-field-type games including road traffic
networks, multi-level building evacuation, millimeter wave wireless
communications, distributed power networks, virus spread over networks, virtual
machine resource management in cloud networks, synchronization of oscillators,
energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201
Invisible control of self-organizing agents leaving unknown environments
In this paper we are concerned with multiscale modeling, control, and
simulation of self-organizing agents leaving an unknown area under limited
visibility, with special emphasis on crowds. We first introduce a new
microscopic model characterized by an exploration phase and an evacuation
phase. The main ingredients of the model are an alignment term, accounting for
the herding effect typical of uncertain behavior, and a random walk, accounting
for the need to explore the environment under limited visibility. We consider
both metrical and topological interactions. Moreover, a few special agents, the
leaders, not recognized as such by the crowd, are "hidden" in the crowd with a
special controlled dynamics. Next, relying on a Boltzmann approach, we derive a
mesoscopic model for a continuum density of followers, coupled with a
microscopic description for the leaders' dynamics. Finally, optimal control of
the crowd is studied. It is assumed that leaders exploit the herding effect in
order to steer the crowd towards the exits and reduce clogging. Locally-optimal
behavior of leaders is computed. Numerical simulations show the efficiency of
the optimization methods in both microscopic and mesoscopic settings. We also
perform a real experiment with people to study the feasibility of the proposed
bottom-up crowd control technique.Comment: in SIAM J. Appl. Math, 201
Boltzmann type control of opinion consensus through leaders
The study of formations and dynamics of opinions leading to the so called
opinion consensus is one of the most important areas in mathematical modeling
of social sciences. Following the Boltzmann type control recently introduced in
[G. Albi, M. Herty, L. Pareschi arXiv:1401.7798], we consider a group of
opinion leaders which modify their strategy accordingly to an objective
functional with the aim to achieve opinion consensus. The main feature of the
Boltzmann type control is that, thanks to an instantaneous binary control
formulation, it permits to embed the minimization of the cost functional into
the microscopic leaders interactions of the corresponding Boltzmann equation.
The related Fokker-Planck asymptotic limits are also derived which allow to
give explicit expressions of stationary solutions. The results demonstrate the
validity of the Boltzmann type control approach and the capability of the
leaders control to strategically lead the followers opinion
Kinetic description of optimal control problems and applications to opinion consensus
In this paper an optimal control problem for a large system of interacting
agents is considered using a kinetic perspective. As a prototype model we
analyze a microscopic model of opinion formation under constraints. For this
problem a Boltzmann-type equation based on a model predictive control
formulation is introduced and discussed. In particular, the receding horizon
strategy permits to embed the minimization of suitable cost functional into
binary particle interactions. The corresponding Fokker-Planck asymptotic limit
is also derived and explicit expressions of stationary solutions are given.
Several numerical results showing the robustness of the present approach are
finally reported.Comment: 25 pages, 18 figure
Mean-Field Pontryagin Maximum Principle
International audienceWe derive a Maximum Principle for optimal control problems with constraints given by the coupling of a system of ordinary differential equations and a partial differential equation of Vlasov-type with smooth interaction kernel. Such problems arise naturally as Gamma-limits of optimal control problems constrained by ordinary differential equations, modeling, for instance, external interventions on crowd dynamics by means of leaders. We obtain these first-order optimality conditions in the form of Hamiltonian flows in the Wasserstein space of probability measures with forward-backward boundary conditions with respect to the first and second marginals, respectively. In particular, we recover the equations and their solutions by means of a constructive procedure, which can be seen as the mean-field limit of the Pontryagin Maximum Principle applied to the optimal control problem for the discretized density, under a suitable scaling of the adjoint variables
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