131433 20240209155915.0 doi 10.1109/TCYB.2018.2805695 sideral 106896 ART-2019-106896 eng Martinez Cantin, Ruben Universidad de Zaragoza (orcid)0000-0002-6741-844X Funneled Bayesian Optimization for Design, Tuning and Control of Autonomous Systems 2019 In this paper, we tackle several problems that appear in robotics and autonomous systems: algorithm tuning, automatic control, and intelligent design. All those problems share in common that they can be mapped to global optimization problems where evaluations are expensive. Bayesian optimization (BO) has become a fundamental global optimization algorithm in many problems where sample efficiency is of paramount importance. BO uses a probabilistic surrogate model to learn the response function and reduce the number of samples required. Gaussian processes (GPs) have become a standard surrogate model for their flexibility to represent a distribution over functions. In a black-box settings, the common assumption is that the underlying function can be modeled with a stationary GP. In this paper, we present a novel kernel function specially designed for BO, that allows nonstationary behavior of the surrogate model in an adaptive local region. This kernel is able to reconstruct nonstationarity even with the irregular sampling distribution that arises from BO. Furthermore, in our experiments, we found that this new kernel results in an improved local search (exploitation), without penalizing the global search (exploration) in many applications. We provide extensive results in well-known optimization benchmarks, machine learning hyperparameter tuning, reinforcement learning, and control problems, and UAV wing optimization. The results show that the new method is able to outperform the state of the art in BO both in stationary and nonstationary problems. info:eu-repo/grantAgreement/ES/MINECO/DPI2015-65962-R info:eu-repo/semantics/closedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 11.079 2019 AUTOMATION & CONTROL SYSTEMS 1 / 63 = 0.016 2019 Q1 T1 COMPUTER SCIENCE, CYBERNETICS 1 / 22 = 0.045 2019 Q1 T1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 5 / 136 = 0.037 2019 Q1 T1 4.393 2019 Electrical and Electronic Engineering 2019 Q1 Computer Science Applications 2019 Q1 Software 2019 Q1 Human-Computer Interaction 2019 Q1 Information Systems 2019 Q1 Control and Systems Engineering 2019 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 49, 4 (2019), 1489-1500 IEEE trans. cybern. (Print) IEEE transactions on cybernetics 2168-2267 1932136 http://zaguan.unizar.es/record/131433/files/texto_completo.pdf Versión publicada 3374304 http://zaguan.unizar.es/record/131433/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:131433 articulos driver 2024-02-09-14:28:11 ARTICLE