This dissertation is concerned with finite state machine-based technology for modeling natural language. Finite-state machines have proven to be efficient computational devices in modeling natural language phenomena in morphology and phonology. Because of their mathematical closure properties, finite-state machines can be manipulated and combined in many flexible ways that closely resemble formalisms used in different areas of linguistics to describe natural language. The use of finite-state transducers in constructing natural language parsers and generators has proven to be a versatile approach to describing phonological alternation, morphological constraints and morphotactics, and syntactic phenomena on the phrase level.The main contributions of this dissertation are the development of a new model of multitape automata, the development of a new logic formalism that can substitute for regular expressions in constructing complex automata, and adaptations of these techniques to solving classical construction problems relating to finite-state transducers, such as modeling reduplication and complex phonological replacement rules.The multitape model presented here goes hand-in-hand with the logic formalism, the latter being a necessary step to constructing the former. These multitape automata can then be used to create entire morphological and phonological grammars, and can also serve as a neutral intermediate tool to ease the construction of automata for other purposes.The construction of large-scale finite-state models for natural language grammars is a very delicate process. Making any solution practicable requires great care in the efficient implementation of low-level tasks such as converting regular expressions, logical statements, sets of constraints, and replacement rules to automata or finite transducers. To support the overall endeavor of showing the practicability of the logical and multitape extensions proposed in this thesis, a detailed treatment of efficient implementation of finite-state construction algorithms for natural language purposes is also presented.

O TESTE baseado em modelos visa a possibilitar a derivação de conjuntos de casos de teste a partir de especificações formais, tais como Máquinas de Estados Finitos. Os conjuntos de teste podem ser obtidos tanto pelos métodos clássicos de geração quanto por alguma abordagem ad hoc. Procura-se obter um conjunto de teste que consiga detectar todos os possíveis defeitos de uma implementação e possua tamanho reduzido para que a sua aplicação seja factível. Por questões de ordem prática, pode não ser possível a aplicação de todo o conjunto de teste gerado. Desse modo, um subconjunto de casos de teste deve ser selecionado, ou seja, uma minimização do conjunto de teste deve ser realizada. No entanto, é fundamental que a minimização reduza o custo de aplicação dos testes, mas mantenha a efetividade em revelar defeitos. Neste trabalho, propõe-se um algoritmo de minimização de conjuntos de teste para Máquinas de Estados Finitos. O algoritmo baseia-se em condições de suficiência para que a completude em relação à detecção de defeitos seja mantida. O algoritmo foi utilizado em dois diferentes contextos. Utilizou-se o algoritmo com conjuntos de teste gerados de forma aleatória para verificar a minimização obtida. O algoritmo também foi utilizado para reduzir o esforço em se obter um conjunto completo em relação à detecção de defeitos / THE Model-based testing aims at generating test suites from formal specifications, such as Finite State Machines. Test suites can be obtained either from classical test derivation methods or from some ad-hoc approach. It is desirable to produce a test suite which detects all possible faults of an implementation and has small size, so that its application can be feasible. For practical reasons, the application of the generated test suite may not be possible. Therefore, a subset of test cases should be selected, i.e., a test suite minimization should be performed. However, it is important that the minimization reduces the test application cost, but keeps the effectiveness in revealing faults. In this work, an algorithm is proposed for the minimization of test suites generated from Finite State Machines. The algorithm is based on sufficient conditions, so that test suite completeness can be maintained. The algorithm was used in two different contexts. It was used with randomly generated test suites to verify the minimization obtained. The algorithm was also used to reduce the effort of obtaining a test suite with full fault coverage

O teste baseado em modelos visa à derivação de casos de teste a partir de modelos produzidos ao longo do desenvolvimento de software. Nesse contexto, as Máquinas de Estados Finitos têm sido amplamente pesquisadas e utilizadas para derivação de seqüências de teste. Para isso, vários métodos de geração de seqüências de teste têm sido desenvolvidos há várias décadas. O objetivo desses métodos é a obtenção de um conjunto de teste que seja capaz de revelar os defeitos de uma implementação. Entretanto, muitas vezes os conjuntos gerados são muito grandes, o que torna sua aplicação inviável. Trabalhos recentes definiram condições que podem ser utilizadas para investigar mecanismos de redução de casos de teste. Este trabalho apresenta uma estratégia para a redução de conjuntos de seqüências de teste a partir de Máquinas de Estados Finitos com base em condições de suficiência. A estratégia baseia-se na combinação de seqüências de um conjunto de teste, de forma a reduzir o número de seqüências e o tamanho delas, mantendo a completude do conjunto. São apresentadas seis abordagens de redução baseadas na estratégia proposta, as quais foram implementadas em uma ferramenta. Para avaliar as abordagens foram conduzidos estudos experimentais, os quais também serviram para inferir sobre as características e propriedades de cada abordagem. Além disso, um estudo de caso com MEFs reais também foi realizado / Model-based testing aims at generating test cases from models produced along the software development process. In this context, Finite State Machines (FSM) have been largely investigated and used for generating test sequences. In the past decades, several test generation methods have been proposed to obtain test suites that are able to reveal implementation faults. Nevertheless, most of the generated test suites are huge, thus hindering their application in practice. Recent research has defined new sufficient conditions that can be employed in mechanisms for reducing the length of test sequences. This work presents a strategy based on sufficient conditions for reducing the length of test cases derived from FSMs. Our strategy is based on sequence combination of a test suite, aiming to reduce the number of sequences and their length, however still keeping full fault coverage. Six reduction approaches are presented based on the proposed strategy and implemented in a tool. In order to evaluate the strategy, we conducted experimental studies that identified characteristics and properties for each of the six proposed approaches. Moreover, a case study with real-world FSMs was performed

Máquinas de Estados Finitos, além de suas inúmeras aplicações, são amplamente utilizadas na Engenharia de Software para modelar especificações de sistemas. Nesses modelos, projetistas podem inserir, inadvertidamente, estados redundantes, ou seja, que exibem o mesmo comportamento. A eliminação desses estados traz diversos benefícios para as atividades que utilizam o modelo, como menor complexidade e menos recursos físicos para implementação. O processo de eliminação desses estados é denominado minimização, e pode ser realizado em tempo polinomial para máquinas completamente especificadas. Por outro lado, a minimização de máquinas parciais, cuja especificação não cobre todo o domínio de entrada, somente pode ser obtida em tempo polinomial com o uso de abordagens não determinísticas, ou seja, trata-se de um problema NP-Completo. Este trabalho apresenta uma estratégia para a minimização de máquinas de estados finitos parciais que faz o uso de heurísticas e otimizações para tornar o processo mais eficiente. Visando mensurar tal ganho de eficiência, foram realizados experimentos, nos quais os tempos de execução de uma implementação do método proposto foram medidos, juntamente com os tempos de implementações de dois outros métodos conhecidos. Os resultados mostraram vantagens significativas de performance para o novo método em relação aos métodos anteriores / Finite State Machines are largely used on Software Engineering to model systems specifications. In these models, designers may inadvertently include redundant states, i.e., states which exhibit the same input/output behavior. The absence of such states brings benefits to the modeling activities, reducing the complexity and taking less physical resources on implementations. The process of eliminating redundant states is known as minimization, and can be accomplished in polynomial time for completely specified machines. On the other hand, the minimization of partially specified machines, i.e., machines which have undefined behavior for some inputs, can only be done in polynomial time when non-deterministic approaches are applied. It is a known NP-Complete problem. This work presents a deterministic approach to minimize incompletely specified Finite State Machines, using heuristics and optimizations to accomplish the task more efficiently. In order to measure the performance improvements, experiments were done, observing the running time of an implementation of the proposed method, along with running times of implementations of two other known methods. The results revealed a significant performance advantage when using the proposed approach

O TESTE baseado em modelos visa a possibilitar a derivação de conjuntos de casos de teste a partir de especificações formais, tais como Máquinas de Estados Finitos. Os conjuntos de teste podem ser obtidos tanto pelos métodos clássicos de geração quanto por alguma abordagem ad hoc. Procura-se obter um conjunto de teste que consiga detectar todos os possíveis defeitos de uma implementação e possua tamanho reduzido para que a sua aplicação seja factível. Por questões de ordem prática, pode não ser possível a aplicação de todo o conjunto de teste gerado. Desse modo, um subconjunto de casos de teste deve ser selecionado, ou seja, uma minimização do conjunto de teste deve ser realizada. No entanto, é fundamental que a minimização reduza o custo de aplicação dos testes, mas mantenha a efetividade em revelar defeitos. Neste trabalho, propõe-se um algoritmo de minimização de conjuntos de teste para Máquinas de Estados Finitos. O algoritmo baseia-se em condições de suficiência para que a completude em relação à detecção de defeitos seja mantida. O algoritmo foi utilizado em dois diferentes contextos. Utilizou-se o algoritmo com conjuntos de teste gerados de forma aleatória para verificar a minimização obtida. O algoritmo também foi utilizado para reduzir o esforço em se obter um conjunto completo em relação à detecção de defeitos / THE Model-based testing aims at generating test suites from formal specifications, such as Finite State Machines. Test suites can be obtained either from classical test derivation methods or from some ad-hoc approach. It is desirable to produce a test suite which detects all possible faults of an implementation and has small size, so that its application can be feasible. For practical reasons, the application of the generated test suite may not be possible. Therefore, a subset of test cases should be selected, i.e., a test suite minimization should be performed. However, it is important that the minimization reduces the test application cost, but keeps the effectiveness in revealing faults. In this work, an algorithm is proposed for the minimization of test suites generated from Finite State Machines. The algorithm is based on sufficient conditions, so that test suite completeness can be maintained. The algorithm was used in two different contexts. It was used with randomly generated test suites to verify the minimization obtained. The algorithm was also used to reduce the effort of obtaining a test suite with full fault coverage

O teste baseado em modelos tem como objetivo auxiliar a atividade de testes, gerando conjuntos de casos de teste a partir de modelos, como Máquinas de Estados Finitos (MEFs). Diversos métodos de geração de conjuntos de caso de teste têm sido propostos ao longo das últimas décadas, com algumas contribuições recentes. Dentre esses trabalhos, há os que geram seqüências de verificação que são conjuntos de caso de teste formados por uma única seqüência e que são capazes de detectar os defeitos de uma implementação cujo comportamento pode ser modelado a partir de uma MEF. Neste trabalho é proposto um algoritmo de geração de seqüências de verificação que tem a finalidade de gerar seqüências menores que as seqüências geradas pelos métodos existentes. O algoritmo, que é baseado na técnica de algoritmos genéticos e nas condições de suficiência para a completude de casos de teste, consiste basicamente em criar novas seqüências a partir de seqüências menores. Por meio de mutações, novas seqüências são geradas pelo algoritmo. As condições de suficiência são utilizadas para determinar quais seqüências geradas são seqüências de verificação. Também são apresentados neste trabalho os estudos experimentais realizados para determinar o comportamento do algoritmo diante de diferentes contextos / Model-based testing aims at aiding the testing activity, generating test cases from models such as Finite State Machines (FSM). Several test cases generation methods have been proposed along the last decades, with some recent contributions. Among these works, there are those that generate checking sequences, which are test cases formed by a single sequence and which are capable of detecting faults in an implementation whose behavior can be modeled as an FSM. This work proposes a checking sequences generation algorithm which aims at generating sequences smaller than the sequences generated by existing methods. The algorithm, which is based on the genetic algorithms technique and sufficient conditions for completeness of test cases, basically consists of creating new sequences from small sequences. Through mutations, new sequences are generated by the algorithm. The suffcient conditions are used to determine which sequences are checking sequences. Experimental studies are presented in this work to determine the behavior of the algorithm on different contexts

Transducers are finite state automata with an output. In this thesis, we attempt to classify sequences that can be constructed by iteratively applying a transducer to a given word. We begin exploring this problem by considering sequences of words that can be produced by iterative application of a transducer to a given input word, i.e., identifying sequences of words of the form w, t(w), t²(w), . . . We call such sequences transducer recognizable. Also we introduce the notion of "recognition of a sequence in context", which captures the possibility of concatenating prefix and suffix words to each word in the sequence, so a given sequence of words becomes transducer recognizable. It turns out that all finite and periodic sequences of words of equal length are transducer recognizable. We also show how to construct a deterministic transducer with the least number of states recognizing a given sequence. To each transducer t we associate a two-dimensional language L²(t) consisting of blocks of symbols in the following way. The first row, w, of each block is in the input language of t, the second row is a word that t outputs on input w. Inductively, every subsequent row is a word outputted by the transducer when its preceding row is read as an input. We show a relationship of the entropy values of these two-dimensional languages to the entropy values of the one-dimensional languages that appear as input languages for finite state transducers.

<p>The aim of the main work in the thesis is to investigate Incremental Evolution methods for designing a suitable behavior arbitration mechanism for behavior-based (BB) robot controllers for autonomous mobile robots performing tasks of higher complexity. The challenge of designing effective controllers for autonomous mobile robots has been intensely studied for few decades. Control Theory studies the fundamental control principles of robotic systems. However, the technological progress allows, and the needs of advanced manufacturing, service, entertainment, educational, and mission tasks require features beyond the scope of the standard functionality and basic control. Artificial Intelligence has traditionally looked upon the problem of designing robotics systems from the high-level and top-down perspective: given a working robotic device, how can it be equipped with an intelligent controller. Later approaches advocated for better robustness, modifiability, and control due to a bottom-up layered incremental controller and robot building (Behavior-Based Robotics, BBR). Still, the complexity of programming such system often requires manual work of engineers. Automatic methods might lead to systems that perform task on demand without the need of expert robot programmer. In addition, a robot programmer cannot predict all the possible situations in the robotic applications. Automatic programming methods may provide flexibility and adaptability of the robotic products with respect to the task performed. One possible approach to automatic design of robot controllers is Evolutionary Robotics (ER). Most of the experiments performed in the field of ER have achieved successful learning of target task, while the tasks were of limited complexity. This work is a marriage of incremental idea from the BBR and automatic programming of controllers using ER. Incremental Evolution allows automatic programming of robots for more complex tasks by providing a gentle and easy-to understand support by expertknowledge — division of the target task into sub-tasks. We analyze different types of incrementality, devise new controller architecture, implement an original simulator compatible with hardware, and test it with various incremental evolution tasks for real robots. We build up our experimental field through studies of experimental and educational robotics systems, evolutionary design, distributed computation that provides the required processing power, and robotics applications. University research is tightly coupled with education. Combining the robotics research with educational applications is both a useful consequence as well as a way of satisfying the necessary condition of the need of underlying application domain where the research work can both reflect and base itself.</p>

In dieser Arbeit wird ein Verfahren für robustes Parsing von uneingeschränktem natürlichsprachlichen Text mit gewichteten Transduktoren erarbeitet. Es werden zwei linguistische Theorien, das Chunking und das syntaktische Tagging, vorgestellt, die sich besonders für die praktische Anwendung mit Finite-State Maschinen eignen. Über die formalen Grundlagen, die es möglich machen, Finite-State Maschinen zu modellieren, werden existierende Ansätze vorgestellt, die diese linguistischen Theorien mit Finite-State Maschinen realisieren. <br>Jedoch sind diese Ansätze in vieler Hinsicht problematisch. Es wird gezeigt, dass sich Probleme lösen lassen, indem Disambiguierungsstrategien durch Constraints realisiert werden, die als Gewicht bzw. Semiring vorliegen. Durch die Bestimmung des besten Pfades ist dann eine Disambiguierung möglich. Das Verfahren bewegt sich zwischen einem Low- und High-Level Parsing und behandelt flache Dependenzstrukturen. Für die Analyse wird eine rudimentäre Grammatik für das Deutsche entwickelt. Durch eine Implementierung wird letztlich der Ansatz getestet.

The aim of the main work in the thesis is to investigate Incremental Evolution methods for designing a suitable behavior arbitration mechanism for behavior-based (BB) robot controllers for autonomous mobile robots performing tasks of higher complexity. The challenge of designing effective controllers for autonomous mobile robots has been intensely studied for few decades. Control Theory studies the fundamental control principles of robotic systems. However, the technological progress allows, and the needs of advanced manufacturing, service, entertainment, educational, and mission tasks require features beyond the scope of the standard functionality and basic control. Artificial Intelligence has traditionally looked upon the problem of designing robotics systems from the high-level and top-down perspective: given a working robotic device, how can it be equipped with an intelligent controller. Later approaches advocated for better robustness, modifiability, and control due to a bottom-up layered incremental controller and robot building (Behavior-Based Robotics, BBR). Still, the complexity of programming such system often requires manual work of engineers. Automatic methods might lead to systems that perform task on demand without the need of expert robot programmer. In addition, a robot programmer cannot predict all the possible situations in the robotic applications. Automatic programming methods may provide flexibility and adaptability of the robotic products with respect to the task performed. One possible approach to automatic design of robot controllers is Evolutionary Robotics (ER). Most of the experiments performed in the field of ER have achieved successful learning of target task, while the tasks were of limited complexity. This work is a marriage of incremental idea from the BBR and automatic programming of controllers using ER. Incremental Evolution allows automatic programming of robots for more complex tasks by providing a gentle and easy-to understand support by expertknowledge — division of the target task into sub-tasks. We analyze different types of incrementality, devise new controller architecture, implement an original simulator compatible with hardware, and test it with various incremental evolution tasks for real robots. We build up our experimental field through studies of experimental and educational robotics systems, evolutionary design, distributed computation that provides the required processing power, and robotics applications. University research is tightly coupled with education. Combining the robotics research with educational applications is both a useful consequence as well as a way of satisfying the necessary condition of the need of underlying application domain where the research work can both reflect and base itself.