Master's thesis is dealing with translation based on coupled finite automaton model. Coupled finite automaton contains input and output automaton. The input automaton makes syntactic analysis with an input string. Used rules from the input automaton control the output automaton, which generates an output string. In thesis is described a way of determinisation of the input automaton without loss of information about rules used in original automaton. The determinizitaion is divided into two parts - for finite and infinite translation specified by transducers. Then is presented a new pair automaton with increased computing power. This increased computing power consists in replace of input or output or just a part of automaton by context free grammar.

Backtracking CSP solvers provide a powerful framework for search and reasoning. The aim of constraint learning is increase global reasoning power by learning new constraints to boost reasoning and hopefully reduce search effort. In this thesis constraint learning is developed in several ways to make it faster and more powerful. First, lazy explanation generation is introduced, where explanations are generated as needed rather than continuously during propagation. This technique is shown to be effective is reducing the number of explanations generated substantially and consequently reducing the amount of time taken to complete a search, over a wide selection of benchmarks. Second, a series of experiments are undertaken investigating constraint forgetting, where constraints are discarded to avoid time and space costs associated with learning new constraints becoming too large. A major empirical investigation into the overheads introduced by unbounded constraint learning in CSP is conducted. This is the first such study in either CSP or SAT. Two significant results are obtained. The first is that typically a small percentage of learnt constraints do most propagation. While this is conventional wisdom, it has not previously been the subject of empirical study. The second is that even constraints that do no effective propagation can incur significant time overheads. Finally, the use of forgetting techniques from the literature is shown to significantly improve the performance of modern learning CSP solvers, contradicting some previous research. Finally, learning is generalised to use disjunctions of arbitrary constraints, where before only disjunctions of assignments and disassignments have been used in practice (g-nogood learning). The details of the implementation undertaken show that major gains in expressivity are available, and this is confirmed by a proof that it can save an exponential amount of search in practice compared with g-nogood learning. Experiments demonstrate the promise of the technique.

Risers são elementos que fazem a conexão física entre a plataforma e o poço. Eles são fundamentais na explotação de petróleo no mar. O projeto destes elementos é, cada vez mais, uma tarefa desafiadora, devido ao aumento da profundidade de explotação, que pode tornar inviável a utilização de uma configuração mais tradicional, como a catenária. Para viabilizar a explotação através de risers, surgem outras configurações, entre elas a lazy-wave, que foi estudada neste texto. Um riser deve atender a critérios de engenharia quando submetido a diversas condições ambientais. Cada uma destas condições é simulada numericamente o que, devido à complexidade do problema, demanda um grande tempo computacional. Considerando-se que, dado um campo, existe um grande número de possibilidades de configurações de risers, o tempo necessário para uma busca exaustiva pela melhor configuração é grande demais, tornando o resultado do projeto altamente dependente da experiência do projetista. Neste trabalho foi proposta uma abordagem diferente, atacando o problema por três frentes: o uso de técnicas de otimização para tomada de decisão sobre as configurações a serem simuladas, com o objetivo de diminuir o número de simulações necessárias, a realização das simulações através de modelos capazes de diminuir o tempo de simulação de cada condição ambiental, e o uso de computação paralela, por meio da qual o trabalho computacional é dividido entre vários núcleos, permitindo maior rapidez na obtenção dos resultados. Para aplicar as técnicas de otimização, foi necessário transformar o projeto de um riser em um problema de otimização, definindo variáveis de projeto, restrições e função objetivo. Em seguida estudaram-se os métodos de otimização aplicáveis ao problema e estes foram implementados em uma ferramenta computacional. Os modelos utilizados para realização das análises dinâmica e estática foram trabalhados tanto para generalização quanto para aumento de robustez e de confiabilidade. A ferramenta desenvolvida foi aplicada a um caso real e os resultados apresentados, através dos quais foi estudada a influência das condições ambientais (movimentos do topo, correntezas e offsets) utilizadas. Dois algoritmos heurísticos e quatro de programação matemática foram também comparados, os primeiros tanto em sua versão serial quanto na paralela. Comparações entre diferentes funções objetivo foram feitas, tendo o melhor resultado sido obtido minimizando a máxima amplitude de tensão dinâmica. Através da minimização desta função, a resposta dinâmica do riser é aprimorada, tendo sido obtidos resultados que mostram que é possível conseguir em pouco tempo e de maneira automática uma configuração que satisfaz os critérios de engenharia aplicáveis a este tipo de projeto e que é a melhor de acordo com uma medida objetiva de eficiência. / Risers are elements that physically connect the platform to the well. They are fundamental elements in sea oil exploitation. Their project is a challenging task, due to the continuous increase in exploitation depth, which can turn infeasible the use of more traditional configurations, such as the free-hanging. To make the exploitation feasible, other configurations are possible, among them the lazy-wave, which was studied in this text. A riser must fulfill engineering criteria under several environmental conditions. Each of these environmental conditions is numerically simulated and, due to the complexity of the problem, the required computational time is high. If one considers that, in a given field, a great number of riser possibilities exists, the time required for a comprehensive search for the best configuration is too large, making the project outcome highly dependent on the designers experience. In this work a different approach was proposed, in three fronts: the use of an optimization technique to decide which configurations will be simulated; in order to reduce the number of necessary simulations, the execution of the simulations using models which are capable of reducing simulation time for each environmental condition; and the use of parallel computing, through which the computational burden is divided among several cores, leading to a faster solution. In order to apply an optimization technique, it was necessary to transform a riser design into an optimization problem, defining design variables, restrictions and objective function. Then, applicable optimization methods were studied and implemented in a computational tool. The models used for the execution of the dynamic and static analyses were generalized and tweaked, so that a better reliability and robustness could be achieved. The developed tool was applied to a real case and the results presented, through which were studied the influence of environmental conditions (top movements, currents and offsets). Two heuristic and four mathematical programming optimization algorithms were also compared, the first both in serial and parallel versions. Comparisons among different objective functions were made, with the best result being obtained by minimizing the maximum dynamic stress amplitude. Through the minimization of this function, the dynamic response of the riser is improved, with results which show that it is possible to obtain in a short time and in an automated way a configuration that fulfills the applicable engineering criteria and that is the best, according to an objective performance measure.

<p>Bounded rationality is the study of how human cognition with limited capacity is adapted to handle the complex information structures in the environment. This thesis argues that in order to understand the bounded rationality of decision processes, it is necessary to develop decision theories that are computational process models based upon basic cognitive and perceptual mechanisms. The main goal of this thesis is to show that models of perceptual categorization based on the storage of exemplars and retrieval of similar exemplars whenever a new object is encountered (D. L. Medin & M. M. Schaffer, 1978), can be an important contribution to theories of decision making. Study I proposed, PROBEX (PROBabilities from Exemplars), a model for inferences from generic knowledge. It is a “lazy” algorithm that presumes no pre-computed abstractions. In a computer simulation it was found to be a powerful decision strategy, and it was possible to fit the model to human data in a psychologically plausible way. Study II was a theoretical investigation that found that PROBEX was very robust in conditions where the decision maker has very little information, and that it worked well even under the worst circumstances. Study III empirically tested if humans can learn to use exemplar based or one reason decision making strategies (G. Gigerenzer, P. Todd, & the ABC Research Group, 1999) where it is appropriate in a two-alternative choice task. Experiment 1 used cue structure and presentation format as independent variables, and participants easily used one reason strategies if the decision task presented the information as normal text. The participants were only able to use exemplars if they were presented as short strings of letters. Experiment 2 failed to accelerate learning of exemplar use during the decision phase, by prior exposure to exemplars in a similar task. In conclusion, this thesis supports that there are at least two modes of decision making, which are boundedly rational if they are used in the appropriate context. Exemplar strategies may, contrary to study II, only be used late in learning, and the conditions for learning need to be investigated further.</p>

Bounded rationality is the study of how human cognition with limited capacity is adapted to handle the complex information structures in the environment. This thesis argues that in order to understand the bounded rationality of decision processes, it is necessary to develop decision theories that are computational process models based upon basic cognitive and perceptual mechanisms. The main goal of this thesis is to show that models of perceptual categorization based on the storage of exemplars and retrieval of similar exemplars whenever a new object is encountered (D. L. Medin &amp; M. M. Schaffer, 1978), can be an important contribution to theories of decision making. Study I proposed, PROBEX (PROBabilities from Exemplars), a model for inferences from generic knowledge. It is a “lazy” algorithm that presumes no pre-computed abstractions. In a computer simulation it was found to be a powerful decision strategy, and it was possible to fit the model to human data in a psychologically plausible way. Study II was a theoretical investigation that found that PROBEX was very robust in conditions where the decision maker has very little information, and that it worked well even under the worst circumstances. Study III empirically tested if humans can learn to use exemplar based or one reason decision making strategies (G. Gigerenzer, P. Todd, &amp; the ABC Research Group, 1999) where it is appropriate in a two-alternative choice task. Experiment 1 used cue structure and presentation format as independent variables, and participants easily used one reason strategies if the decision task presented the information as normal text. The participants were only able to use exemplars if they were presented as short strings of letters. Experiment 2 failed to accelerate learning of exemplar use during the decision phase, by prior exposure to exemplars in a similar task. In conclusion, this thesis supports that there are at least two modes of decision making, which are boundedly rational if they are used in the appropriate context. Exemplar strategies may, contrary to study II, only be used late in learning, and the conditions for learning need to be investigated further.

Many programs are executed according to the conventional, eager execution order, for which verification of execution costs is well-understood. However, there are other execution orders in use. One such order in common use is lazy execution or lazy evaluation, which is mostly demand-driven. Laziness supports better decompositions of algorithms, e.g., into modular producers and consumers, which enables compositional reasoning of answer correctness, but then timing correctness is more elusive. This thesis gives a formal method for verifying lazy timing, compositional with respect to program structure; it is an extension of a predicative programming theory. Predicative programming theories are formal methods that unify both specifications and programs as predicates or boolean-typed expressions over memory state and other quantities of interest. Their strengths are mathematical simplicity and support of program development and verification by incremental refinements. Among these theories, Hehner's a Practical Theory of Programming has the further strength of leaving termination and timing open rather than a built-in, and therefore is a flexible substrate for various timing schemes corresponding to various execution strategies. We use this substrate for our method for lazy timing. This thesis also proves soundness of the eager timing scheme in Hehner's work with respect to an eager operational semantics, and our lazy timing scheme with respect to a lazy operational semantics. Thus, if refinements promise an upper time bound, then execution actually stops within that time. Lastly, this thesis outlines a space of more operational semantics. It is possible ground for more execution strategies.

Many programs are executed according to the conventional, eager execution order, for which verification of execution costs is well-understood. However, there are other execution orders in use. One such order in common use is lazy execution or lazy evaluation, which is mostly demand-driven. Laziness supports better decompositions of algorithms, e.g., into modular producers and consumers, which enables compositional reasoning of answer correctness, but then timing correctness is more elusive. This thesis gives a formal method for verifying lazy timing, compositional with respect to program structure; it is an extension of a predicative programming theory. Predicative programming theories are formal methods that unify both specifications and programs as predicates or boolean-typed expressions over memory state and other quantities of interest. Their strengths are mathematical simplicity and support of program development and verification by incremental refinements. Among these theories, Hehner's a Practical Theory of Programming has the further strength of leaving termination and timing open rather than a built-in, and therefore is a flexible substrate for various timing schemes corresponding to various execution strategies. We use this substrate for our method for lazy timing. This thesis also proves soundness of the eager timing scheme in Hehner's work with respect to an eager operational semantics, and our lazy timing scheme with respect to a lazy operational semantics. Thus, if refinements promise an upper time bound, then execution actually stops within that time. Lastly, this thesis outlines a space of more operational semantics. It is possible ground for more execution strategies.

Briteback Explore is a service provided by the company Briteback. The service provides a survey tool to its users that allows access to results through the downloading of a comma-separated values (CSV) -file. What was built was a visualization model that provides the survey tool’s users with a graphical representation of the data directly through the service. Providing an overview of the survey results that lessens the amount of information overload perceived by the user while giving the data a structure. A survey based off of the System Usability Scale (SUS) evaluation method was then performed to display to what degree users found the new feature usable. Results showed a satisfactory high score on the SUS adjective rating scale.

Risers são elementos que fazem a conexão física entre a plataforma e o poço. Eles são fundamentais na explotação de petróleo no mar. O projeto destes elementos é, cada vez mais, uma tarefa desafiadora, devido ao aumento da profundidade de explotação, que pode tornar inviável a utilização de uma configuração mais tradicional, como a catenária. Para viabilizar a explotação através de risers, surgem outras configurações, entre elas a lazy-wave, que foi estudada neste texto. Um riser deve atender a critérios de engenharia quando submetido a diversas condições ambientais. Cada uma destas condições é simulada numericamente o que, devido à complexidade do problema, demanda um grande tempo computacional. Considerando-se que, dado um campo, existe um grande número de possibilidades de configurações de risers, o tempo necessário para uma busca exaustiva pela melhor configuração é grande demais, tornando o resultado do projeto altamente dependente da experiência do projetista. Neste trabalho foi proposta uma abordagem diferente, atacando o problema por três frentes: o uso de técnicas de otimização para tomada de decisão sobre as configurações a serem simuladas, com o objetivo de diminuir o número de simulações necessárias, a realização das simulações através de modelos capazes de diminuir o tempo de simulação de cada condição ambiental, e o uso de computação paralela, por meio da qual o trabalho computacional é dividido entre vários núcleos, permitindo maior rapidez na obtenção dos resultados. Para aplicar as técnicas de otimização, foi necessário transformar o projeto de um riser em um problema de otimização, definindo variáveis de projeto, restrições e função objetivo. Em seguida estudaram-se os métodos de otimização aplicáveis ao problema e estes foram implementados em uma ferramenta computacional. Os modelos utilizados para realização das análises dinâmica e estática foram trabalhados tanto para generalização quanto para aumento de robustez e de confiabilidade. A ferramenta desenvolvida foi aplicada a um caso real e os resultados apresentados, através dos quais foi estudada a influência das condições ambientais (movimentos do topo, correntezas e offsets) utilizadas. Dois algoritmos heurísticos e quatro de programação matemática foram também comparados, os primeiros tanto em sua versão serial quanto na paralela. Comparações entre diferentes funções objetivo foram feitas, tendo o melhor resultado sido obtido minimizando a máxima amplitude de tensão dinâmica. Através da minimização desta função, a resposta dinâmica do riser é aprimorada, tendo sido obtidos resultados que mostram que é possível conseguir em pouco tempo e de maneira automática uma configuração que satisfaz os critérios de engenharia aplicáveis a este tipo de projeto e que é a melhor de acordo com uma medida objetiva de eficiência. / Risers are elements that physically connect the platform to the well. They are fundamental elements in sea oil exploitation. Their project is a challenging task, due to the continuous increase in exploitation depth, which can turn infeasible the use of more traditional configurations, such as the free-hanging. To make the exploitation feasible, other configurations are possible, among them the lazy-wave, which was studied in this text. A riser must fulfill engineering criteria under several environmental conditions. Each of these environmental conditions is numerically simulated and, due to the complexity of the problem, the required computational time is high. If one considers that, in a given field, a great number of riser possibilities exists, the time required for a comprehensive search for the best configuration is too large, making the project outcome highly dependent on the designers experience. In this work a different approach was proposed, in three fronts: the use of an optimization technique to decide which configurations will be simulated; in order to reduce the number of necessary simulations, the execution of the simulations using models which are capable of reducing simulation time for each environmental condition; and the use of parallel computing, through which the computational burden is divided among several cores, leading to a faster solution. In order to apply an optimization technique, it was necessary to transform a riser design into an optimization problem, defining design variables, restrictions and objective function. Then, applicable optimization methods were studied and implemented in a computational tool. The models used for the execution of the dynamic and static analyses were generalized and tweaked, so that a better reliability and robustness could be achieved. The developed tool was applied to a real case and the results presented, through which were studied the influence of environmental conditions (top movements, currents and offsets). Two heuristic and four mathematical programming optimization algorithms were also compared, the first both in serial and parallel versions. Comparisons among different objective functions were made, with the best result being obtained by minimizing the maximum dynamic stress amplitude. Through the minimization of this function, the dynamic response of the riser is improved, with results which show that it is possible to obtain in a short time and in an automated way a configuration that fulfills the applicable engineering criteria and that is the best, according to an objective performance measure.

The increasing amount of available semistructured data demands efficient mechanisms to store, process, and search an enormous corpus of data to encourage its global adoption. Current techniques to store semistructured documents either map them to relational databases, or use a combination of flat files and indexes. These two approaches result in a mismatch between the tree-structure of semistructured data and the access characteristics of the underlying storage devices. Furthermore, the inefficiency of XML parsing methods has slowed down the large-scale adoption of XML into actual system implementations. The recent development of lazy parsing techniques is a major step towards improving this situation, but lazy parsers still have significant drawbacks that undermine the massive adoption of XML. Once the processing (storage and parsing) issues for semistructured data have been addressed, another key challenge to leverage semistructured data is to perform effective information discovery on such data. Previous works have addressed this problem in a generic (i.e. domain independent) way, but this process can be improved if knowledge about the specific domain is taken into consideration. This dissertation had two general goals: The first goal was to devise novel techniques to efficiently store and process semistructured documents. This goal had two specific aims: We proposed a method for storing semistructured documents that maps the physical characteristics of the documents to the geometrical layout of hard drives. We developed a Double-Lazy Parser for semistructured documents which introduces lazy behavior in both the pre-parsing and progressive parsing phases of the standard Document Object Model's parsing mechanism. The second goal was to construct a user-friendly and efficient engine for performing Information Discovery over domain-specific semistructured documents. This goal also had two aims: We presented a framework that exploits the domain-specific knowledge to improve the quality of the information discovery process by incorporating domain ontologies. We also proposed meaningful evaluation metrics to compare the results of search systems over semistructured documents.