Design of Complex Engineered Systems Using Multiagent Coordination

This thesis is the combination of two research publications working toward a unified strategy in which the design of complex engineered systems can be completed using a multiagent coordination approach. Current engineered system modeling techniques segment large complex models into multiple groups to be simulated independently. These methods restrict the evaluations of such complex systems, as their failure properties are typically unknown until they are experienced in operation. In an effort to help engineers to design complex engineered systems, this research proposes that a distributed yet non-legislated approach can be used in the design processes by splitting up the overall system into specific teams. The approach specifically hypothesizes that multiagent credit assignment can be used to effectively determine how to properly incentivize subsystem designers so that the global set of system-level objectives can be achieved. The first publication presents a multiagent systems based approach for designing a self-replicating robotic manufacturing factory in space. The simulation in this work is able to present the coordination of the agents during the construction of the factory as the parameters of the learning algorithm are changed. The results show the advantage of using a learning algorithm to design a large system. The second publication presents a hybrid approach to design complex engineered systems, providing a method in which design decisions can be reconciled without the need for either detailed interaction models or external legislating mechanisms. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm can effectively design a complex engineered system. Each publication utilized a system model to illustrate and simulate the methods and potential results. By designing complex systems with a multiagent coordination approach, a design methodology can be developed in an effort to reduce design uncertainty and provide mechanisms through which the system level impact of decisions can be estimated without explicitly modeling such interactions

Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry

Aim To determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). Methods Observational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. Results As of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p = 0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.211, 95%CI 0.067-0.667, p = 0.008). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). Conclusions Hospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality

Compilación de Proyectos de Investigacion de 1984-2002

Instituto Politecnico Nacional. UPIICS