The cognitive approach of consumer behavior emphasizes the purchase decision process, where pre-purchase alternatives evaluation is a stage and the focus of this study. Surfwear is a specific segment of clothes industry that offers a large quantity of goods and brands to its targetmarket. The study search to identify the more important evaluative criteria was superposed in a set of surfwear brands (evoked set) and results in a choice. Survey was employed in a non probabilistic sample of consumers in Fortaleza/CE. The results made evident that brand is the most important criteria and the evoked set is constituted by 1.5 brands. Still includes study restrictions and suggestions for new researches. / A abordagem cognitiva do comportamento do consumidor focaliza o processo de decisÃo de compra, onde a avaliaÃÃo de alternativas prÃ-compra à uma de suas etapas e o objeto deste estudo. O surfwear à um segmento especÃfico dentro da indÃstria do vestuÃrio, que disponibiliza um grande nÃmero de produtos e marcas para seu mercado-alvo. O estudo buscou identificar quais os critÃrios de carÃter utilitÃrio mais importantes foram aplicados sobre um conjunto de marcas de surfwear (conjunto de consideraÃÃo) e que resultou na compra de um produto. Foi utilizado o survey em uma amostra nÃo probabilÃstica de consumidores da cidade de Fortaleza/CE. Os resultados obtidos evidenciaram que a marca à o critÃrio mais importante e que o conjunto de consideraÃÃo à composto por 1,5 marcas. Incluem-se ainda as restriÃÃes do estudo e as sugestÃes de novas pesquisas.

BRCA1 and BRCA2 are gene mutations that drastically increase chances of developing breast and ovarian cancers, up to 20-fold, for women. A genetic blood test is used to detect BRCA mutations. Though these mutations occur in one of every 400 in the general population (excluding Ashkenazi Jewish ethnicity), they are present in most cases of hereditary breast and ovarian cancer patients. Hence, it is common practice for the physicians to require genetic testing for those that fit the rules as recommended by the National Cancer Comprehensive Network. However, data from the Myriad Laboratory, the only provider of the test until 2013, show that over 70 percent of those tested are negative for BRCA mutations [1]. As there are significant costs and psychological trauma associated with having to go through the test, there is a need for more comprehensive rules for determining who should be tested. Once the presence of BRCA is identified via testing, the next challenge for both mutation carriers and their physicians is to select the most appropriate types and timing of intervention actions. Organizations such as the American Cancer Society suggest drastic intervention actions such as prophylactic surgeries and intense breast screenings. These actions vary significantly in their cost, cancer incidence prevention ability, and can have major side effects potentially resulting in reproduction inability or death. Effectiveness of these intervention actions is also age dependent. In this dissertation, both an analytical and an optimization framework are presented. The analytical framework uses supervised machine learning models on extended family history of cancers, and personal and medical information from a recent nationwide survey study of women who have been referred for genetic testing for the presence of a BRCA mutation. This framework provides the potential mutation carriers as well as their physician with an estimate of the likelihood of having the mutations. The optimization framework uses a Markov decision process (MDP) model to find cost-optimal and/or quality-adjusted life years (QALYs) optimal intervention strategies for those tested positive for a BRCA mutation. This framework uses a dynamic approach to address this problem. The decisions are made more robust by considering the variation in estimates of the transition probabilities by using a robust version of the MDP model. This research study delivers an innovative decision support tool that enables physicians and genetic consultants predict the population at high risk of breast and ovarian cancers more accurately. For those identified with presence of the BRCA mutation, the decision support tool offers effective intervention strategies considering either minimizing cost or maximizing QALYs to prevent incidence of cancers.

It is envisioned that future cyber-physical systems will provide a more convenient living and working environment. However, such systems need inevitably to collect and process privacy-sensitive information. That means the benefits come with potential privacy leakage risks. Nowadays, this privacy issue receives more attention as a legal requirement of the EU General Data Protection Regulation. In this thesis, privacy-by-design approaches are studied where privacy enhancement is realized through taking privacy into account in the physical layer design. This work focuses in particular on cyber-physical systems namely sensor networks and smart grids. Physical-layer performance and privacy leakage risk are assessed by hypothesis testing measures. First, a sensor network in the presence of an informed eavesdropper is considered. Extended from the traditional hypothesis testing problems, novel privacy-preserving distributed hypothesis testing problems are formulated. The optimality of deterministic likelihood-based test is discussed. It is shown that the optimality of deterministic likelihood-based test does not always hold for an intercepted remote decision maker and an optimal randomized decision strategy is completely characterized by the privacy-preserving condition. These characteristics are helpful to simplify the person-by-person optimization algorithms to design optimal privacy-preserving hypothesis testing networks. Smart meter privacy becomes a significant issue in the development of smart grid technology. An innovative scheme is to exploit renewable energy supplies or an energy storage at a consumer to manipulate meter readings from actual energy demands to enhance the privacy. Based on proposed asymptotic hypothesis testing measures of privacy leakage, it is shown that the optimal privacy-preserving performance can be characterized by a Kullback-Leibler divergence rate or a Chernoff information rate in the presence of renewable energy supplies. When an energy storage is used, its finite capacity introduces memory in the smart meter system. It is shown that the design of an optimal energy management policy can be cast to a belief state Markov decision process framework. / <p>QC 20170815</p>

This thesis deals with the purchase behavior and habits of consumers in the retail chain COOP, particularly cooperative COOP Hořovice. The work consists of two main parts -- theoretical and practical. The theoretical part solves the issue of purchase behavior of consumer and the factors that influence on consumer during all phases of their purcasing decisions, from a general perspective. Regarding the solving issue work also provides information about private labels, which are typical for retail. Furthermore it includes basic knowledge of marketing research and its phases. The practical part is beginning with important data about the cooperative COOP Hoovice (history, basic information, offered services and private labels). Recommendations for improvements are based on the course of research and its results interpretation.

Planejamento Probabilístico estuda os problemas de tomada de decisão sequencial de um agente, em que as ações possuem efeitos probabilísticos, modelados como um processo de decisão markoviano (Markov Decision Process - MDP). Dadas a função de transição de estados probabilística e os valores de recompensa das ações, é possível determinar uma política de ações (i.e., um mapeamento entre estado do ambiente e ações do agente) que maximiza a recompensa esperada acumulada (ou minimiza o custo esperado acumulado) pela execução de uma sequência de ações. Nos casos em que o modelo MDP não é completamente conhecido, a melhor política deve ser aprendida através da interação do agente com o ambiente real. Este processo é chamado de aprendizado por reforço. Porém, nas aplicações em que não é permitido realizar experiências no ambiente real, por exemplo, operações de venda, é possível realizar o aprendizado por reforço sobre uma amostra de experiências passadas, processo chamado de aprendizado por reforço em lote (Batch Reinforcement Learning). Neste trabalho, estudamos técnicas de aprendizado por reforço em lote usando um histórico de interações passadas, armazenadas em um banco de dados de processos, e propomos algumas formas de melhorar os algoritmos existentes. Como um estudo de caso, aplicamos esta técnica no aprendizado de políticas para o processo de venda de impressoras de grande formato, cujo objetivo é a construção de um sistema de recomendação de ações para vendedores iniciantes. / Probabilistic planning studies the problems of sequential decision-making of an agent, in which actions have probabilistic effects, and can be modeled as a Markov decision process (MDP). Given the probabilities and reward values of each action, it is possible to determine an action policy (in other words, a mapping between the state of the environment and the agent\'s actions) that maximizes the expected reward accumulated by executing a sequence of actions. In cases where the MDP model is not completely known, the best policy needs to be learned through the interaction of the agent in the real environment. This process is called reinforcement learning. However, in applications where it is not allowed to perform experiments in the real environment, for example, sales process, it is possible to perform the reinforcement learning using a sample of past experiences. This process is called Batch Reinforcement Learning. In this work, we study techniques of batch reinforcement learning (BRL), in which learning is done using a history of past interactions, stored in a processes database. As a case study, we apply this technique for learning policies in the sales process for large format printers, whose goal is to build a action recommendation system for beginners sellers.

Raster images represent the treatment options of how the forest will be cut. Economic benefits from cutting the forest will be generated after the treatment is selected and executed. Existing raster images have many clusters and small sizes, this becomes the principal cause of overhead. If we can fully explore the relationship among the raster images and combine the old data sets according to the optimization algorithm to generate a new raster image, then this result will surpass the existing raster images and create higher economic benefits.    The question of this project is can we create a dynamic model that treats the updating pixel’s status as an agent selecting options for an empty raster image in response to neighborhood environmental and landscape parameters. This project is trying to explore if it is realistic to use deep reinforcement learning to generate new and superior raster images. Finally, this project aims to explore the feasibility, usefulness, and effectiveness of deep reinforcement learning algorithms in optimizing existing treatment options.    The problem was modeled as a Markov decision process, in which the pixel to be updated was an agent of the empty raster image, which would determine the choice of the treatment option for the current empty pixel. This project used the Deep Q learning neural network model to calculate the Q values. The temporal difference reinforcement learning algorithm was applied to predict future rewards and to update model parameters.   After the modeling was completed, this project set up the model usefulness experiment to test the usefulness of the model. Then the parameter correlation experiment was set to test the correlation between the parameters and the benefit of the model. Finally, the trained model was used to generate a larger size raster image to test its effectiveness.

The stochastic shortest path problem lies at the heart of many questions in the formal verification of probabilistic systems. It asks to find a scheduler resolving the non-deterministic choices in a weighted Markov decision process (MDP) that minimizes or maximizes the expected accumulated weight before a goal state is reached. In the classical setting, it is required that the scheduler ensures that a goal state is reached almost surely. For the analysis of systems without guarantees on the occurrence of an event of interest (reaching a goal state), however, schedulers that miss the goal with positive probability are of interest as well. We study two non-classical variants of the stochastic shortest path problem that drop the restriction that the goal has to be reached almost surely. These variants ask for the optimal partial expectation, obtained by assigning weight 0 to paths not reaching the goal, and the optimal conditional expectation under the condition that the goal is reached, respectively. Both variants have only been studied in structures with non-negative weights. We prove that the decision versions of these non-classical stochastic shortest path problems in MDPs with arbitrary integer weights are at least as hard as the Positivity problem for linear recurrence sequences. This Positivity problem is an outstanding open number-theoretic problem, closely related to the famous Skolem problem. A decid- ability result for the Positivity problem would imply a major breakthrough in analytic number theory. The proof technique we develop can be applied to a series of further problems. In this way, we obtain Positivity-hardness results for problems addressing the termination of one-counter MDPs, the satisfaction of energy objectives, the satisfaction of cost constraints and the computation of quantiles, the conditional value-at-risk – an important risk measure – for accumulated weights, and the model-checking problem of frequency-LTL. Despite these Positivity-hardness results, we show that the optimal values for the non-classical stochastic shortest path problems can be achieved by weight-based deter- ministic schedulers and that the optimal values can be approximated in exponential time. In MDPs with non-negative weights, it is known that optimal partial and conditional expectations can be computed in exponential time. These results rely on the existence of a saturation point, a bound on the accumulated weight above which optimal schedulers can behave memorylessly. We improve the result for partial expectations by showing that the least possible saturation point can be computed efficiently. Further, we show that a simple saturation point also allows us to compute the optimal conditional value-at-risk for the accumulated weight in MDPs with non-negative weights. Moreover, we introduce the notions of long-run probability and long-run expectation addressing the long-run behavior of a system. These notions quantify the long-run average probability that a path property is satisfied on a suffix of a run and the long-run average expected amount of weight accumulated before the next visit to a target state, respectively. We establish considerable similarities of the corresponding optimization problems with non-classical stochastic shortest path problems. On the one hand, we show that the threshold problem for optimal long-run probabilities of regular co-safety properties is Positivity-hard via the Positivity-hardness of non-classical stochastic shortest path problems. On the other hand, we show that optimal long-run expectations in MDPs with arbitrary integer weights and long-run probabilities of constrained reachability properties (a U b) can be computed in exponential time using the existence of a saturation point.

Across a range of sectors, including transportation and healthcare, the use of automation to assist humans with increasingly complex tasks is also demanding that such systems are more interactive with human users. Given the role of cognitive factors in human decision-making during their interactions with automation, models enabling human cognitive state estimation and prediction could be used by autonomous systems to appropriately adapt their behavior. However, accomplishing this requires mathematical models of human cognitive state evolution that are suitable for algorithm design. In this thesis, a computational model of coupled human trust and self-confidence dynamics is proposed. The dynamics are modeled as a partially observable Markov decision process that leverages behavioral and self-report data as observations for estimation of the cognitive states. The use of an asymmetrical structure in the emission probability functions enables labeling and interpretation of the coupled cognitive states. The model is trained and validated using data collected from 340 participants. Analysis of the transition probabilities shows that the model captures nuanced effects, in terms of participants' decisions to rely on an autonomous system, that result as a function of the combination of their trust in the automation and self-confidence. Implications for the design of human-aware autonomous systems are discussed, particularly in the context of human trust and self-confidence calibration.

Projekt har använts av människor sedan början av sin tid och är idag en vanlig arbetsform bland många branscher och organisationer. Projekt är inte bara effektiva när det kommer till att åstadkomma resultat utan också för att implementera organisatoriska förändringar och anses således spela en viktig roll i införandet av hållbar utveckling bland organisationer. Projekts användbarhet har lett till att en stor mängd forskning dedikerats till ämnet projektledning vilket bland annat bidragit med en nyanserad portfolio av projekttyper och projektmetoder. Däremot har den inte försett organisationer med en förståelse för varför så många finns och hur man väljer bland dem. Forskning pekar också på att det finns ett positivt samband mellan framgångsrika projekt och lämplig projektmetodik vilket indikerar att valet av projektmetod har en stor betydelse för projektframgång och därmed också införandet av hållbar utveckling bland organisationer. Fortfarande vet vi väldigt lite om just processen att välja projektmetod. I litteraturen inom innovationsdiffusion, däremot, har man sedan långt tillbaka sökt förklaringar till varför organisationer väljer att adoptera eller avvisa innovationer och bidragit med flera teorier inom detta ämne. Målet med denna studie var därför att undersöka om innovationsdiffusionsteorier är applicerbara i ett projektmetodsammanhang och i vilken utsträckning hållbar utveckling används som bedömningskriterium med syftet att försöka beskriva vad som ligger bakom organisationers val av projektmetod. Detta har inneburit en identifikation av påverkansfaktorer inom ramen för dessa teorier, för att avgöra deras relevans i sammanhanget, samt inom hållbar utveckling. En kvalitativ forskningsmetod med en deduktiv ansats på teori tillämpades och data ackumulerades via intervjuer från både näringslivsanställda och forskare. Resultatet indikerar att valet av projektmetod bland organisationer med stor sannolikhet påverkas av ett flertal yttre faktorer: 1) Makthavande organisationer som hämmar företag att jobba agilt genom offentlig upphandling 2) Konsultfirmor som ”säljer” olika projektmetoder genom deras kapacitet att övertyga 3) Media som influerar beslutsfattare genom dess förmåga att skapa och kapa trender och 4) Andra organisationer som influerar andra beslutsfattare att imitera deras val av projektmetod genom deras positiva resultat eller egenskaper. / Projects have been used by people since the beginning of their time and are now a common form of work among many industries and organizations. Projects are not only effective when it comes to achieving results but also for implementing organizational changes and are thus considered to play an important role in the introduction of sustainable development among organizations. The usefulness of projects has led to a large amount of research being dedicated to the topic of project management, which has, among other things, contributed with a nuanced portfolio of project types and project methods. However, it has not provided organizations with an understanding of why so many exist and how to choose between them. Studies also indicate that there is a positive relationship between successful projects and appropriate project methodology, which indicates that the choice of project method has a great importance for project success and thus also the introduction of sustainable development among organizations. Still, we know very little about the process of choosing a project method. In the literature on the diffusion of innovations, however, researchers have sought explanations for why organizations choose to adopt or reject innovations and contributed with several theories in this subject. The aim of this study was therefore to investigate whether innovation diffusion theories are applicable in a project method context and to what extent sustainable development is used as an assessment criterion with the purpose of trying to describe what lies behind organizations' choice of project method. This has involved identifying influencing factors within the framework of these theories, to determine their relevance in the context, as well as within sustainable development. A qualitative research method with a deductive approach to theory was applied and data was accumulated via interviews from both business employees and researchers. The result indicates that the choice of project method among organizations is very likely to be influenced by a number of external factors: 1) Powerful organizations which inhibit companies from working agile through public procurement, 2) Consultancy firms which "sell" different project methods through their capacity to convince, 3) Media which influence decision makers through its ability to create and cut trends and 4) Other organizations which influence decision makers to imitate their choice of method trough their positive results or attributes.

En planification théorique de la décision, le cadre des Processus Décisionnels Markoviens Factorisés (Factored Markov Decision Process, FMDP) a produit des algorithmes efficaces de résolution des problèmes de décisions séquentielles dans l'incertain. L'efficacité de ces algorithmes repose sur des structures de données telles que les Arbres de Décision ou les Diagrammes de Décision Algébriques (ADDs). Ces techniques de planification sont utilisées en Apprentissage par Renforcement par l'architecture SDYNA afin de résoudre des problèmes inconnus de grandes tailles. Toutefois, l'état-de-l'art des algorithmes d'apprentissage, de programmation dynamique et d'apprentissage par renforcement utilisés par SDYNA, requière que le problème soit spécifié uniquement à l'aide de variables binaires et/ou utilise des structures améliorables en termes de compacité. Dans ce manuscrit, nous présentons nos travaux de recherche visant à élaborer et à utiliser une structure de donnée plus efficace et moins contraignante, et à l'intégrer dans une nouvelle instance de l'architecture SDYNA. Dans une première partie, nous présentons l'état-de-l'art de la modélisation de problèmes de décisions séquentielles dans l'incertain à l'aide de FMDP. Nous abordons en détail la modélisation à l'aide d'DT et d'ADDs.Puis nous présentons les ORFGs, nouvelle structure de données que nous proposons dans cette thèse pour résoudre les problèmes inhérents aux ADDs. Nous démontrons ainsi que les ORFGs s'avèrent plus efficaces que les ADDs pour modéliser les problèmes de grandes tailles. Dans une seconde partie, nous nous intéressons à la résolution des problèmes de décision dans l'incertain par Programmation Dynamique. Après avoir introduit les principaux algorithmes de résolution, nous nous attardons sur leurs variantes dans le domaine factorisé. Nous précisons les points de ces variantes factorisées qui sont améliorables. Nous décrivons alors une nouvelle version de ces algorithmes qui améliore ces aspects et utilise les ORFGs précédemment introduits. Dans une dernière partie, nous abordons l'utilisation des FMDPs en Apprentissage par Renforcement. Puis nous présentons un nouvel algorithme d'apprentissage dédié à la nouvelle structure que nous proposons. Grâce à ce nouvel algorithme, une nouvelle instance de l'architecture SDYNA est proposée, se basant sur les ORFGs ~:~l'instance SPIMDDI. Nous testons son efficacité sur quelques problèmes standards de la littérature. Enfin nous présentons quelques travaux de recherche autour de cette nouvelle instance. Nous évoquons d'abord un nouvel algorithme de gestion du compromis exploration-exploitation destiné à simplifier l'algorithme F-RMax. Puis nous détaillons une application de l'instance SPIMDDI à la gestion d'unités dans un jeu vidéo de stratégie en temps réel. / In decision theoretic planning, the factored framework (Factored Markovian Decision Process, FMDP) has produced several efficient algorithms in order to resolve large sequential decision making under uncertainty problems. The efficiency of this algorithms relies on data structures such as decision trees or algebraïc decision diagrams (ADDs). These planification technics are exploited in Reinforcement Learning by the architecture SDyna in order to resolve large and unknown problems. However, state-of-the-art learning and planning algorithms used in SDyna require the problem to be specified uniquely using binary variables and/or to use improvable data structure in term of compactness. In this book, we present our research works that seek to elaborate and to use a new data structure more efficient and less restrictive, and to integrate it in a new instance of the SDyna architecture. In a first part, we present the state-of-the-art modeling tools used in the algorithms that tackle large sequential decision making under uncertainty problems. We detail the modeling using decision trees and ADDs. Then we introduce the Ordered and Reduced Graphical Representation of Function, a new data structure that we propose in this thesis to deal with the various problems concerning the ADDs. We demonstrate that ORGRFs improve on ADDs to model large problems. In a second part, we go over the resolution of large sequential decision under uncertainty problems using Dynamic Programming. After the introduction of the main algorithms, we see in details the factored alternative. We indicate the improvable points of these factored versions. We describe our new algorithm that improve on these points and exploit the ORGRFs previously introduced. In a last part, we speak about the use of FMDPs in Reinforcement Learning. Then we introduce a new algorithm to learn the new datastrcture we propose. Thanks to this new algorithm, a new instance of the SDyna architecture is proposed, based on the ORGRFs : the SPIMDDI instance. We test its efficiency on several standard problems from the litterature. Finally, we present some works around this new instance. We detail a new algorithm for efficient exploration-exploitation compromise management, aiming to simplify F-RMax. Then we speak about an application of SPIMDDI to the managements of units in a strategic real time video game.