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dc.contributor.authorKontoudis, Georgios Pantelisen
dc.date.accessioned2022-01-26T09:00:13Zen
dc.date.available2022-01-26T09:00:13Zen
dc.date.issued2022-01-25en
dc.identifier.othervt_gsexam:33797en
dc.identifier.urihttp://hdl.handle.net/10919/107923en
dc.description.abstractIn this dissertation, we propose decentralized and scalable algorithms for Gaussian process (GP) training and prediction in multi-agent systems. The first challenge is to compute a spatial field that represents underwater acoustic communication performance from a set of measurements. We compare kriging to cokriging with vehicle range as a secondary variable using a simple approximate linear-log model of the communication performance. Next, we propose a model-based learning methodology for the prediction of underwater acoustic performance using a realistic propagation model. The methodology consists of two steps: i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters; and ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The second challenge is to perform predictions at unvisited locations with a team of agents and limited inter-agent information exchange. To decentralize the implementation of GP training, we employ the alternating direction method of multipliers (ADMM). A closed-form solution of the decentralized proximal ADMM is provided for the case of GP hyper-parameter training with maximum likelihood estimation. Multiple aggregation techniques for GP prediction are decentralized with the use of iterative and consensus methods. In addition, we propose a covariance-based nearest neighbor selection strategy that enables a subset of agents to perform predictions. Empirical evaluations illustrate the efficiency of the proposed methodsen
dc.format.mediumETDen
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectGaussian Processesen
dc.subjectKrigingen
dc.subjectDistributed Optimizationen
dc.subjectUnderwater Acoustic Communicationsen
dc.subjectDecentralized Networksen
dc.titleCommunication-Aware, Scalable Gaussian Processes for Decentralized Explorationen
dc.typeDissertationen
dc.contributor.departmentElectrical Engineeringen
dc.description.degreeDoctor of Philosophyen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.leveldoctoralen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineElectrical Engineeringen
dc.contributor.committeechairStilwell, Daniel J.en
dc.contributor.committeememberWoolsey, Craig A.en
dc.contributor.committeememberTokekar, Pratapen
dc.contributor.committeememberWilliams, Ryan K.en
dc.contributor.committeememberSaad, Waliden
dc.description.abstractgeneralIn this dissertation, we propose decentralized and scalable algorithms for collaborative multiagent learning. Mobile robots, such as autonomous underwater vehicles (AUVs), can use predictions of communication performance to anticipate where they are likely to be connected to the communication network. The first challenge is to predict the acoustic communication performance of AUVs from a set of measurements. We compare two methodologies using a simple model of communication performance. Next, we propose a model-based learning methodology for the prediction of underwater acoustic performance using a realistic model. The methodology first estimates the covariance matrix and then predicts the communication performance. The efficiency of the framework is validated with simulations and experimental data from field trials. The second challenge regards the efficient execution of Gaussian processes using multiple agents and communicating as little as possible. We propose decentralized algorithms that facilitate local computations at the expense of inter-agent communications. Moreover, we propose a nearest neighbor selection strategy that enables a subset of agents to participate in the prediction. Illustrative examples with real world data are provided to validate the efficiency of the algorithms.en


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