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Contributions to open multi-agent systems : consensus, optimization and epidemics

Abstract : In this thesis we address several problems formulated in an open multi-agent system (OMAS) scenario where the set of agents can change in time, independently of the evolution of the dynamics associated with the system. We analyze the case of OMAS formulated in a fixed finite size network and we use two approaches for the analysis of the systems. In the first approach we consider scenarios characterized by activation/deactivation of agents such that at each time instant a different subset of active agents can interact in the system. In the second approach we study scenarios characterized by replacements of agents where at a specific time instant, an agent can be replaced while the rest of the agents remain the same. In this case, all the agents are able to interact at all time.Three different problems are considered in this thesis: randomized consensus, resource allocation problem and epidemics. First, we analyze the problem of randomized consensus subject to additive noise where different subset of agents exchange information at each iteration. We define a noise index based on the expected mean squared error and we derive upper bounds. Then, we consider the resource allocation problem where agents can be replaced during the implementation of an optimization algorithm. For this problem, we analyze two different algorithms: weighted gradient descent and random coordinate descent. For the weighted gradient descent, we evaluate the performance of the algorithm in an OMAS subject to packet losses by defining appropriate performance metrics. For the random coordinate descent algorithm, we study the convergence to the minimizer in an OMAS and we provide an alternative analysis using tools inspired from online optimization. Finally, we study a SIS epidemic in continuous time subject to replacements of agents during the evolution of the disease. We perform the analysis using an aggregate function and deriving upper bounds for its asymptotic behavior.
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https://theses.hal.science/tel-03858738
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Submitted on : Thursday, November 17, 2022 - 6:49:11 PM
Last modification on : Tuesday, November 22, 2022 - 8:36:48 AM

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  • HAL Id : tel-03858738, version 1

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Renato Sebastian Vizuete Haro. Contributions to open multi-agent systems : consensus, optimization and epidemics. Automatic. Université Paris-Saclay, 2022. English. ⟨NNT : 2022UPAST114⟩. ⟨tel-03858738⟩

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