Pruning dominated policies in multiobjective Pareto Q-learning

The solution for a Multi-Objetive Reinforcement Learning problem is a set of Pareto optimal policies. MPQ-learning is a recent algorithm that approximates the whole set of all Pareto-optimal deterministic policies by directly generalizing Q-learning to the multiobjective setting. In this paper we present a modification of MPQ-learning that avoids useless cyclical policies and thus improves the number of training steps required for convergence.Supported by: the Spanish Government, Agencia Estatal de Investigaci´on (AEI) and European Union, Fondo Europeo de Desarrollo Regional (FEDER), grant TIN2016-80774-R (AEI/FEDER, UE); and Plan Propio de Investigación de la Universidad de Málaga - Campus de Excelencia Internacional Andalucía Tech

Learning Bayesian Networks for Student Modeling

In the last decade, there has been a growing interest in using Bayesian Networks (BN) in the student modelling problem. This increased interest is probably due to the fact that BNs provide a sound methodology for this difficult task. In order to develop a Bayesian student model, it is necessary to define the structure (nodes and links) and the parameters. Usually the structure can be elicited with the help of human experts (teachers), but the difficulty of the problem of parameter specification is widely recognized in this and other domains. In the work presented here we have performed a set of experiments to compare the performance of two Bayesian Student Models, whose parameters have been specified by experts and learnt from data respectively. Results show that both models are able to provide reasonable estimations for knowledge variables in the student model, in spite of the small size of the dataset available for learning the parametersUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Context-aware Assessment Using QR-codes

In this paper we present the implementation of a general mechanism to deliver tests based on mobile devices and matrix codes. The system is an extension of Siette, and has not been specifically developed for any subject matter. To evaluate the performance of the system and show some of its capabilities, we have developed a test for a second-year college course on Botany at the School of Forestry Engineering. Students were equipped with iPads and took an outdoor test on plant species identification. All students were able to take and complete the test in a reasonable time. Opinions expressed anonymously by the students in a survey about the usability of the system and the usefulness of the test were very favorable. We think that the application presented in this paper can broaden the applicability of automatic assessment techniques.The presentation of this work has been co-founded by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A temporal difference method for multi-objective reinforcement learning

This work describes MPQ-learning, an temporal-difference method that approximates the set of all non-dominated policies in multi-objective Markov decision problems, where rewards are vectors and each component stands for an objective to maximize. Unlike other approximations to Multi-objective Reinforcement Learning, MPQ-learning does not require additional parameters or preference information, and can be applied to non-convex Pareto frontiers. We also present the results of the application of MPQ-learning to some benchmark problems and compare it to a linearization procedure.This work is partially funded by grants TIN2009-14179 (Spanish Government, Plan Nacional de I+D+i) and TIN2016-80774-R (AEI/FEDER, UE) (Spanish Government, Agencia Estatal de Investigación; and European Union, Fondo Europeo de Desarrollo Regional). Manuela Ruiz-Montiel is funded by the Spanish Ministry of Education through the National F.P.U. Program

PQ-learning: aprendizaje por refuerzo multiobjetivo

En este artí culo describimos y analizamos PQ-learning, un algoritmo para problemas de aprendizaje por refuerzo multiobjetivo. El algoritmo es una extensi ón de Q-learning, un algoritmo para problemas de aprendizaje por refuerzo escalares. Al contrario que otros algoritmos, PQ-learning no requiere informaci ón de preferencias sobre los objetivos, es aplicable a problemas con fronteras de Pareto no convexas y permite recuperar a partir de los Q-valores las secuencias de acci ón correspondientes a diferentes polí ticas Pareto- óptimas. PQ-learning ha sido aplicado a dos problemas pertenecientes a un banco de pruebas propuesto en la literatura de aprendizaje por refuerzo multiobjetivoEste trabajo está parcialmente fi nanciado por el Plan Nacional de I+D+I, proyecto TIN2009-14179 (Gobierno de España, Ministerio de Ciencia e Innovaci ón) y por la Universidad de M álaga, Campus de Excelencia Internacional Andaluc ía Tech. Manuela Ruiz-Montiel disfruta de una beca FPU (Gobierno de España, Ministerio de Educación

Multi-objective dynamic programming with limited precision

This paper addresses the problem of approximating the set of all solutions for Multi-objective Markov Decision Processes. We show that in the vast majority of interesting cases, the number of solutions is exponential or even infinite. In order to overcome this difficulty we propose to approximate the set of all solutions by means of a limited precision approach based on White’s multi-objective value-iteration dynamic programming algorithm. We prove that the number of calculated solutions is tractable and show experimentally that the solutions obtained are a good approximation of the true Pareto front.Funding for open access charge: Universidad de Málaga / CBUA. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Funded by the Spanish Government, Agencia Estatal de Investigación (AEI) and European Union, Fondo Europeo de Desarrollo Regional (FEDER), Grant TIN2016-80774-R (AEI/FEDER, UE)

Using machine learning techniques for architectural design tracking: an experimental study of the design of a shelter

In this paper, we present a study aimed at tracking and analysing the design process. More concretely, we intend to explore whether some elements of the conceptual design stage in architecture might have an influence on the quality of the final project and to find and assess common solution pathways in problem-solving behaviour. In this sense, we propose a new methodology for design tracking, based on the application of data analysis and machine learning techniques to data obtained in snapshots of selected design instants. This methodology has been applied in an experimental study, in which fifty-two novice designers were required to design a shelter with the help of a specifically developed computer tool that allowed collecting snapshots of the project at six selected design instants. The snapshots were described according to nine variables. Data analysis and machine learning techniques were then used to extract the knowledge contained in the data. More concretely, supervised learning techniques (decision trees) were used to find strategies employed in higher-quality designs, while unsupervised learning techniques (clustering) were used to find common solution pathways. Results provide evidence that supervised learning techniques allow elucidating the class of the best projects by considering the order of some of the decisions taken. Also, unsupervised learning techniques can find several common problem-solving pathways by grouping projects into clusters that use similar strategies. In this way, our work suggests a novel approach to design tracking, using quantitative analysis methods that can complement and enrich the traditional qualitative approach.This work has been partially funded by the Spanish Government, Agencia Estatal de Investigaci ́on (AEI), and the European Union, Fondo Europeo de Desarrollo Regional (FEDER), grant TIN2016-80774-R (AEI/FEDER, UE). Funding for open access charge: Universidad de Málaga/CBUA

Investigación en Matemáticas, Economía, Ciencias Sociales y Agronomía

Cada trabajo del libro incluye conclusiones para los interesados en las temáticas aludidas y en ellos nos enteramos de aspectos como los siguientes: - El mayor incremento del precio de los insumos como el maíz, sorgo y en menor medida desperdicio de pan, en relación con el menor crecimiento del precio del ganado en pie, dará como consecuencia un desabasto de carne bovina. - El agua es un recurso primordial en las zonas áridas y semiáridas de México, en tanto que su aporte limita la producción de la agricultura. En este estudio se observó que el precio real del agua es muy bajo en relación a otras zonas agrícolas del mundo. - Hoy en día en el país se consumen alrededor de 718 mil barriles diarios de gasolinas, un aproximado de 113.7 millones de litros, una cantidad tan grande que nuestro país se ve en la necesidad de importar cerca del 39 % de las gasolinas que consumimos. - Los jaliscienses radicados en Estados Unidos tienen una mayor capacidad de financiamiento del bienestar en la entidad, que el propio gobierno de ese estado. - México continuará basando sus finanzas públicas y su política de desarrollo económico en la extracción de combustibles fósiles (petróleo). Este modelo acelerará el deterioro y agotamiento de los recursos naturales. -La importancia de la agricultura orgánica radica en que retoma los tres ámbitos de la sustentabilidad; el ámbito ambiental, el económico y el social. - Es fundamental motivar la organización de los productores de haba para que ellos puedan captar una mayor proporción de los altos márgenes de precios que los consumidores están dispuestos a pagar. - Las condiciones del clima afectan a la producción agraria. Debido al fenómeno de cambio climático, es necesario contar con herramientas informáticas que proporcionen información climatológica para poder tomar medidas preventivas a favor de una mayor cantidad y calidad de producción. La herramienta de software permite la consulta del clima por localidades evitando la necesidad de contar con una estación meteorológica

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years