Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Learning Analytics and Interactions in Virtual Learning Environments. A Comparative Study of Typologies and their Relationship with Academic Performance

Analysis of learning data (learning analytics) is a new research field with high growth potential. The main objective of Learning analytics is the analysis of data (interactions being the basic data unit) generated in virtual learning environments, in order to maximize the outcomes of the learning process; however, a consensus has not been reached yet on which interactions must be measured and what is their influence on learning outcomes. This research is grounded on the study of e-learning interaction typologies and their relationship with students? academic performance, by means of a comparative study between different interaction typologies (based on the agents involved, frequency of use and participation mode). The main conclusions are a) that classifications based on agents offer a better explanation of academic performance; and b) that each of the three typologies are able to explain academic performance in terms of some of their components (student-teacher and student-student interactions, evaluating students interactions and active interactions, respectively), with the other components being nonrelevant.

The role of land and water for trade and food production in Latin America: Environmental trade-offs of agricultural intensification versus extensification

One of humanity’s major challenges of the 21st century will be meeting future food demands on an increasingly resource constrained-planet. Global food production will have to rise by 70 percent between 2000 and 2050 to meet effective demand which poses major challenges to food production systems. Doing so without compromising environmental integrity is an even greater challenge. This study looks at the interdependencies between land and water resources, agricultural production and environmental outcomes in Latin America and the Caribbean (LAC), an area of growing importance in international agricultural markets. Special emphasis is given to the role of LAC’s agriculture for (a) global food security and (b) environmental sustainability. We use the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT)—a global dynamic partial equilibrium model of the agricultural sector—to run different future production scenarios, and agricultural trade regimes out to 2050, and assess changes in related environmental indicators. Results indicate that further trade liberalization is crucial for improving food security globally, but that it would also lead to more environmental pressures in some regions across Latin America. Contrasting land expansion versus more intensified agriculture shows that productivity improvements are generally superior to agricultural land expansion, from an economic and environmental point of view. Finally, our analysis shows that there are trade-offs between environmental and food security goals for all agricultural development paths.

The influence of diet on the effectiveness of garlic oil and cinnamaldehyde to manipulate in vitro ruminal fermentation and methane production.

The objective of this study was to evaluate the effects of increasing doses [0 (control: CON), 20, 60, 180 and 540 mg/L incubation medium] of garlic oil (GO) and cinnamaldehyde (CIN) on in vitro ruminal fermentation of two diets. Batch cultures of mixed ruminal microorganisms were inoculated with ruminal fluid from four sheep fed a medium-concentrate diet (MC; 50 : 50 alfalfa hay : concentrate) or four sheep fed a high-concentrate diet (HC; 15 : 85 barley straw : concentrate). Diets MC and HC were representative of those fed to dairy and fattening ruminants, respectively. Samples of each diet were used as incubation substrates for the corresponding inoculum, and the incubation was repeated on 4 different days (four replicates per experimental treatment). There were GO × diet-type and CIN × diet-type interactions (P < 0.001–0.05) for many of the parameters determined, indicating different effects of both oils depending on the diet type. In general, effects of GO were more pronounced for MC compared with HC diet. Supplementation of GO did not affect (P > 0.05) total volatile fatty acid (VFA) production at any dose. For MC diet, GO at 60, 180 and 540 mg/L decreased (P < 0.05) molar proportion of acetate (608, 569 and 547 mmol/mol total VFA, respectively), and increased (P < 0.05) propionate proportion (233, 256 and 268 mmol/mol total VFA, respectively), compared with CON values (629 and 215 mmol/mol total VFA for acetate and propionate, respectively). A minimum dose of 180 mg of GO/L was required to produce similar modifications in acetate and propionate proportions with HC diet, but no effects (P > 0.05) on butyrate proportion were detected. Methane/VFA ratio was reduced (P < 0.05) by GO at 60, 180 and 540 mg/L for MC diet (0.23, 0.16 and 0.10 mol/mol, respectively), and by GO at 20, 60, 180 and 540 mg/L for HC diet (0.19, 0.19, 0.16 and 0.08 mol/mol, respectively), compared with CON (0.26 and 0.21 mol/mol for MC and HC diets, respectively). No effects (P = 0.16–0.85) of GO on final pH and concentrations of NH3-N and lactate were detected. For both diet types, the highest CIN dose decreased (P < 0.05) production of total VFA, gas and methane, which would indicate an inhibition of fermentation. Compared with CON, CIN at 180 mg/L increased (P < 0.05) acetate proportion for the MC (629 and 644 mmol/mol total VFA for CON and CIN, respectively) and HC (525 and 540 mmol/mol total VFA, respectively) diets, without affecting the proportions of any other VFA or total VFA production. Whereas for MC diet CIN at 60 and 180 mg/L decreased (P < 0.05) NH3-N concentrations compared with CON, only a trend (P < 0.10) was observed for CIN at 180 mg/L with the HC diet. Supplementation of CIN up to 180 mg/L did not affect (P = 0.18–0.99) lactate concentrations and production of gas and methane for any diet. The results show that effectiveness of GO and CIN to modify ruminal fermentation may depend on diet type, which would have practical implications if they are confirmed in vivo.

Combine use of Selected Schizosaccharomyces pombe and Lachancea thermotolerans Yeast Strains as an Alternative to the Traditional Malolactic Fermentation in Red Wine Production

Most red wines commercialized in the market use the malolactic fermentationprocess in order to ensure stability from a microbiological point of view. In this secondfermentation, malic acid is converted into L-lactic acid under controlled setups. Howeverthis process is not free from possible collateral effects that on some occasions produceoff-flavors, wine quality loss and human health problems. In warm viticulture regions suchas the south of Spain, the risk of suffering a deviation during the malolactic fermentationprocess increases due to the high must pH. This contributes to produce wines with highvolatile acidity and biogenic amine values. This manuscript develops a new red winemakingmethodology that consists of combining the use of two non-Saccharomyces yeast strains asan alternative to the traditional malolactic fermentation. In this method, malic acid is totallyconsumed by Schizosaccharomyces pombe, thus achieving the microbiological stabilizationobjective, while Lachancea thermotolerans produces lactic acid in order not to reduce andeven increase the acidity of wines produced from low acidity musts. This technique reducesthe risks inherent to the malolactic fermentation process when performed in warm regions.The result is more fruity wines that contain less acetic acid and biogenic amines than thetraditional controls that have undergone the classical malolactic fermentation.

Effect of nano-Si2O and nano-Al2O3 on cement mortars for use in agriculture and livestock production

The effect of nano-silica, nano-alumina and binary combinations on surface hardness, resistance to abrasion and freeze-thaw cycle resistance in cement mortars was investigated. The Vickers hardness, the Los Angeles coefficient (LA) and the loss of mass in each of the freeze–thaw cycles to which the samples were subjected were measured. Four cement mortars CEM I 52.5R were prepared, one as control, and the other three with the additions: 5% nano-Si, 5% nano-Al and mix 2.5% n-Si and 2.5% n-Al. Mortars were tested at 7, 28 and 90 d of curing to determine compression strength, total porosity and pore distribution by mercury intrusion porosimetry (MIP) and the relationship between the CSH gel and Portlandite total by thermal gravimetric analysis (TGA). The capillary suction coefficient and an analysis by a scanning electron microscope (SEM) was made. There was a large increase in Vickers surface hardness for 5% n-Si mortar and a slight increase in resistance to abrasion. No significant difference was found between the mortars with nano-particles, whose LA was about 10.8, classifying them as materials with good resistance to abrasion. The microstructure shows that the addition of n-Si in mortars refines their porous matrix, increases the amount of hydrated gels and generates significant changes in both Portlandite and Ettringite. This produced a significant improvement in freeze–thaw cycle resistance. The effect of n-Al on mortar was null or negative with respect to freeze–thaw cycle resistance.

Global GacA-steered control of cyanide and exoprotease production in Pseudomonas fluorescens involves specific ribosome binding sites

The conserved two-component regulatory system GacS/GacA determines the expression of extracellular products and virulence factors in a variety of Gram-negative bacteria. In the biocontrol strain CHA0 of Pseudomonas fluorescens, the response regulator GacA is essential for the synthesis of extracellular protease (AprA) and secondary metabolites including hydrogen cyanide. GacA was found to exert its control on the hydrogen cyanide biosynthetic genes (hcnABC) and on the aprA gene indirectly via a posttranscriptional mechanism. Expression of a translational hcnA′-′lacZ fusion was GacA-dependent whereas a transcriptional hcnA-lacZ fusion was not. A distinct recognition site overlapping with the ribosome binding site appears to be primordial for GacA-steered regulation. GacA-dependence could be conferred to the Escherichia coli lacZ mRNA by a 3-bp substitution in the ribosome binding site. The gene coding for the global translational repressor RsmA of P. fluorescens was cloned. RsmA overexpression mimicked partial loss of GacA function and involved the same recognition site, suggesting that RsmA is a downstream regulatory element of the GacA control cascade. Mutational inactivation of the chromosomal rsmA gene partially suppressed a gacS defect. Thus, a central, GacA-dependent switch from primary to secondary metabolism may operate at the level of translation.

Effect of reduced myristoylated alanine-rich C kinase substrate expression on hippocampal mossy fiber development and spatial learning in mutant mice: Transgenic rescue and interactions with gene background

The myristoylated alanine-rich C kinase substrate (MARCKS) is a prominent protein kinase C (PKC) substrate in brain that is expressed highly in hippocampal granule cells and their axons, the mossy fibers. Here, we examined hippocampal infrapyramidal mossy fiber (IP-MF) limb length and spatial learning in heterozygous Macs mutant mice that exhibit an ≈50% reduction in MARCKS expression relative to wild-type controls. On a 129B6(N3) background, the Macs mutation produced IP-MF hyperplasia, a significant increase in hippocampal PKCɛ expression, and proficient spatial learning relative to wild-type controls. However, wild-type 129B6(N3) mice exhibited phenotypic characteristics resembling inbred 129Sv mice, including IP-MF hypoplasia relative to inbred C57BL/6J mice and impaired spatial-reversal learning, suggesting a significant contribution of 129Sv background genes to wild-type and possibly mutant phenotypes. Indeed, when these mice were backcrossed with inbred C57BL/6J mice for nine generations to reduce 129Sv background genes, the Macs mutation did not effect IP-MF length or hippocampal PKCɛ expression and impaired spatial learning relative to wild-type controls, which now showed proficient spatial learning. Moreover, in a different strain (B6SJL(N1), the Macs mutation also produced a significant impairment in spatial learning that was reversed by transgenic expression of MARCKS. Collectively, these data indicate that the heterozygous Macs mutation modifies the expression of linked 129Sv gene(s), affecting hippocampal mossy fiber development and spatial learning performance, and that MARCKS plays a significant role in spatial learning processes.