A Geração de Língua Natural (GLN) ocupa-se de atribuir forma lingüística a dados em representação não-lingüística (Reiter & Dale, 2000); a Realização Lingüística (RL), por sua vez, reúne as subtarefas da GLN estritamente dependentes das especificidades da língua-alvo. Este trabalho objetiva a investigação em RL, uma de cujas aplicações mais proeminentes é a construção de módulos geradores de língua-alvo na tradução automática baseada em transferência semântica. Partimos da identificação de três requisitos fundamentais para modelos de RL quais sejam generalidade, instanciabilidade e complexidade e da tensão entre esses requisitos no estado da arte. Argumentamos pela relevância da avaliação formal dos modelos da literatura contra esses critérios e focalizamos em modelos baseados em restrições (Schulte, 2002) como promissores para reconciliar os três requisitos. Nesta classe de modelos, identificamos o recente modelo de Debusmann (2006) Extensible Dependency Grammar (XDG) e sua implementação - o XDG Development Toolkit (XDK) - como uma plataforma especialmente promissora para o desenvolvimento em RL, apesar de jamais utilizada para tal. Nossas contribuições práticas se resumem ao esforço de tornar o XDK mais eficiente e uma formulação da disjunção inerente à lexicalização adequada à XDG, demonstrando suas potenciais vantagens numa sistema de GLN mais completo / Natural Language Generation (NLG) concerns assigning linguistic form to data in nonlinguistic representation (Reiter & Dale, 2000); Linguistic Realization (LR), in turn, comprises all strictly target language-dependent NLG tasks. This work looks into RL systems from the perspective of three fundamental requirements - namely generality, instantiability, and complexity and the tension between them in the state of the art. We argue for the formal evaluation of models against these criteria and focus on constraint-based models (Schulte, 2002) as tools to reconcile them. In this class of models we identify the recent development of Debusmann (2006) - Extensible Dependency Grammar (XDG) - and its implementation - the XDG Development Toolkit (XDK) - as an especially promising platform for RL work, in spite of never having been used as such. Our practical contributions comprehend a successful effort to make the XDK more efficient and a formulation of lexicalization disjunction suitable to XDG, illustrating its potential advantages in a full-fledged NLG system

This Thesis is focused on the arranging of the melody, which is accompanied by jazz chords. It deals with creating a more harmonious voices using Block Voicing method. Distribution to target notes and passing notes is made using techniques of constraint programming (CSP). Passing notes are reharmonized by dominant seventh chord or by parallel chord. Using CSP a bass part is also created. To solve CSP is used Gecode library. The harmonious voices are arranged by Four Part Close Voicing. The application result is a tool for the music arranger.

Le domaine de la planification de tâches a vu de rapides développements au cours de la dernière décennie et des planificateurs sont maintenant capable de trouver des plans de centaines actions en quelques secondes. Malgré ces importants progrès, les systèmes robotiques dépendent toujours d'une architecture réactive avec peu de capacités de délibération sur les futures actions qu'il pourraient faire. Dans cette thèse, nous soutenons qu'une intégration réussie d'un planificateur avec un système robotique ne peut être réussie que si le planificateur a la capacité de raisonner sur des modèles temporels et hiérarchiques. Le temps est en en effet une ressource centrale pour énormément d'activité autonomes tandis que les aspects hiérarchiques sont critiques pour l'intégration de modules de délibération à différents niveau d'abstraction, dans lequel on reçoit une vue très abstraite d'une activité qui doit être affinée jusqu'à des commandes motrices. Comme première étape dans cette direction, nous commençons par présenter un modèle pour la planification temporelle qui unifie les approches génératives et hiérarchiques. Au centre de ce modèle sont des patrons d'actions temporelles, complétées par une spécification d'un état initial et de l'évolution attendue de l'environnement. De plus, notre modèle permet la spécification de connaissance hiérarchique sur tout ou partie du domaine. Ainsi, notre modèle généralise les approches génératives et HTN tout en supportant une représentation explicite du temps. Ensuite, nous introduisons un algorithme de planification adapté au modèle proposé. Pour supporter les caractéristiques hiérarchiques, nous étendons l'approche classique de planification en l'espace des plan, notamment utilisée dans les planificateurs basés sur les CSP, avec les notions de tâches et de décomposition. L'approche est implémentée dans FAPE (Flexible Acting and Planning Environment) conjointement avec des techniques pour l'analyse automatique de problèmes. Celles-ci sont utilisées au cours de la planification pour guider la recherche d'une solution. Nous montrons que FAPE a des performances comparables avec les meilleurs planificateurs actuels quand utilisé dans une optique de planification générative. L'ajout d'information hiérarchique permet de les surpasser en augmentant encore les performances. Nous étudions ensuite les méthodes habituellement utilisées pour raisonner sur l'incertitude temporelle en planification. Nous relâchons les suppositions classiques d'observabilité totale et proposons des techniques pour raisonner sur les observations nécessaires pour maintenir un plan exécutable. Nous montrons que les dites observations peuvent être détectées durant la planification et traitées incrémentalement en considérant les actions de perceptions appropriées. Pour finir, nous discutons de la place du système de planification proposé comme composant central pour le contrôle d'un robot. Nous démontrons que la prise en compte explicite du temps facilite le monitoring et l'exécution d'actions quand le système doit prendre en compte des événements contingents qui nécessitent d'être observés. Nous bénéficions également des représentations hiérarchiques et par contraintes qui facilitent la réparation de plan et la possibilité d'affiner un plan durant l'exécution. / The field of AI planning has seen rapid progress over the last decade and planners are now able to find plan with hundreds of actions in a matter of seconds. Despite those important progresses, robotic systems still tend to have a reactive architecture with very little deliberation on the course of the plan they might follow. In this thesis, we argue that a successful integration with a robotic system requires the planner to have capacities for both temporal and hierarchical reasoning. The former is indeed a universal resource central in many robot activities while the latter is a critical component for the integration of reasoning capabilities at different abstraction levels, typically starting with a high level view of an activity that is iteratively refined down to motion primitives. As a first step to carry out this vision, we present a model for temporal planning unifying the generative and hierarchical approaches. At the center of the model are temporal action templates, similar to those of PDDL complemented with a specification of the initial state as well as the expected evolution of the environment over time. In addition, our model allows for the specification of hierarchical knowledge possibly with a partial coverage. Consequently, our model generalizes the existing generative and HTN approaches together with an explicit time representation. In the second chapter, we introduce a planning procedure suitable for our planning model. In order to support hierarchical features, we extend the existing Partial-Order Causal Link approach used in many constraintbased planners, with the notions of task and decomposition. We implement it in FAPE (Flexible Acting and Planning Environment) together with automated problem analysis techniques used for search guidance. We show FAPE to have performance similar to state of the art temporal planners when used in a generative setting. The addition of hierarchical information leads to further performance gain and allows us to outperform traditional planners. In the third chapter, we study the usual methods used to reason on temporal uncertainty while planning. We relax the usual assumption of total observability and instead provide techniques to reason on the observations needed to maintain a plan dispatchable. We show how such needed observations can be detected at planning time and incrementally dealt with by considering the appropriate sensing actions. In a final chapter, we discuss the place of the proposed planning system as a central component for the control of a robotic actor. We demonstrate how the explicit time representation facilitates plan monitoring and action dispatching when dealing with contingent events that require observation. We take advantage of the constraint-based and hierarchical representation to facilitate both plan-repair procedures as well opportunistic plan refinement at acting time.

With the increasing complexity of the chip architecture design for meeting different application requirements, the corresponding instruction scheduler of high-level synthesis tool needs to solve complex scheduling problems. Dynamically Reconfigurable Resource Array (DRRA) is a novel architecture based on Coarse-Grained Reconfigurable Architecture (CGRA) on SiLago platform, the instruction scheduler of Vesyla-II, the dedicated High-Level Synthesis (HLS) tool targets for DRRA needs to schedule the specific instruction sets designed for Distributed Two-level Control System (D2LC). This kind of instruction has different lifetimes and is fully cooperative and persistent. Based on these features, the instruction scheduler needs to be applied to the scheduling algorithm under complex constraints. The previously existing naive algorithm shows poor scalability and low efficiency. This thesis attempts to design and implement a new scheduling algorithm to improve the performance of a constraint programming engine-based scheduler. The new scheduling algorithm is based on the heuristic method, the scheduler with this algorithm does the order prediction during the resource scheduling process. Besides, a test bench for meeting different instruction scheduling behavior is also designed, and the test bench could generate the maximum boundary of the schedule to do the performance profiling of the developed algorithm. Several experiments are performed to compare the proposed method against the previous naive algorithm. The execution time and quality of the result are crucial to determine which algorithm has better performance. The experiment result shows that the scheduler with a heuristic algorithm could reduce the execution time and have comparable schedule quality, and it could solve all the test cases, whilst the naive algorithm only can solve part of them. / Med den ökande komplexiteten hos chiparkitekturdesignen för att möta olika applikationskrav, måste motsvarande instruktionsschemaläggare för högnivåsyntesverktyg lösa komplexa schemaläggningsproblem. Dynamically Reconfigurable Resource Array (DRRA) är en ny arkitektur baserad på Coarse-Grained Reconfigurable Architecture (CGRA) på SiLago-plattformen, instruktionsschemaläggaren för Vesyla-II, de dedikerade High Level Synthesis (HLS) verktygsmålen för DRRA behöver för att schemalägga de specifika instruktionsuppsättningar designade för distribuerat tvånivåstyrsystem (D2LC). Denna typ av undervisning har olika livslängder och är helt samarbetsvillig och ihållande. Baserat på dessa funktioner måste instruktionsschemaläggaren appliceras på schemaläggningsalgoritmen under komplexa begränsningar. Den tidigare existerande naiva algoritmen visar dålig skalbarhet och låg effektivitet. Den här avhandlingen försöker designa och implementera en ny schemaläggningsalgoritm för att förbättra prestandan hos en schemaläggare som är baserad på begränsningsprogrammeringsmotorer. Den nya schemaläggningsalgoritmen är baserad på den heuristiska metoden, schemaläggaren med denna algoritm gör ordningsförutsägelsen under resursschemaläggningsprocessen. Dessutom är en testbänk för att möta olika instruktionsschemaläggningsbeteenden också utformad, och testbänken kan generera den maximala gränsen för schemat för att göra prestandaprofileringen av den utvecklade algoritmen. Flera experiment utförs för att jämföra den föreslagna metoden mot den tidigare naiva algoritmen. Exekveringstiden och kvaliteten på resultatet är avgörande för att avgöra vilken algoritm som har bättre prestanda. Experimentresultatet visar att schemaläggaren med en heuristisk algoritm kan minska exekveringstiden och ha jämförbar schemakvalitet, och den kan lösa alla testfall, medan den naiva algoritmen bara kan lösa en del av dem.

The modal satisfiability problem has to date been solved using either a specifically designed algorithm, or by translating the modal logic formula into a different class of problem, such as a first-order logic, a propositional satisfiability problem or a constraint satisfaction problem. These approaches and the solvers developed to support them are surveyed and a synthesis thereof is presented. The translation of a modal K formula into a constraint satisfaction problem, as developed by Brand et al. [18], is further enhanced. The modal formula, which must be in conjunctive normal form, is translated into layered propositional formulae. Each of these layers is translated into a constraint satisfaction problem and solved using the constraint solver ECLiPSe. I extend this translation to deal with reflexive and transitive accessibility relations, thereby providing for the modal logics KT and S4. Two of the difficulties that arise when these accessibility relations are added are that the resultant formula increases considerably in complexity, and that it is no longer in conjunctive normal form (CNF). I eliminate the need for the conversion of the formula to CNF and deal instead with formulae that are in negation normal form (NNF). I apply a number of enhancements to the formula at each modal layer before it is translated into a constraint satisfaction problem. These include extensive simplification, the assignment of a single value to propositional variables that occur only positively or only negatively, and caching the status of the formula at each node of the search tree. All of these significantly prune the search space. The final results I achieve compare favorably with those obtained by other solvers. / Computing / M.Sc. (Computer Science)

Le souhait d’augmenter la part des énergies renouvelables dans le mix énergétique entraine une augmentation des parts des énergies volatiles et non pilotables, et rend donc l’équilibre offre-demande difficile à satisfaire. Une façon d’intégrer ces énergies dans le réseau électrique actuel est d’utiliser de petits moyens de production, de consommation et de stockage répartis sur tout le territoire pour compenser les sous ou sur productions. Afin que ces procédés puissent être intégrés dans le processus d’équilibre offre-demande, ils sont regroupés au sein d’une centrale virtuelle d’agrégation de flexibilité, qui est vue alors comme une centrale virtuelle. Comme pour tout autre moyen de production du réseau, il est nécessaire de déterminer son plan de production. Nous proposons dans un premier temps dans cette thèse une architecture et un mode de gestion pour une centrale d’agrégation composée de n’importe quel type de procédés. Dans un second temps, nous présentons des algorithmes permettant de calculer le plan de production des différents types de procédés respectant toutes leurs contraintes de fonctionnement. Et enfin, nous proposons des approches pour calculer le plan de production de la centrale d’agrégation dans le but de maximiser son gain financier en respectant les contraintes réseau. / The desire to increase the share of renewable energies in the energy mix leads to an increase inshare of volatile and non-controllable energy and makes it difficult to meet the supply-demand balance. A solution to manage anyway theses energies in the current electrical grid is to deploy new energy storage and demand response systems across the country to counter balance under or over production. In order to integrate all these energies systems to the supply and demand balance process, there are gathered together within a virtual flexibility aggregation power plant which is then seen as a virtual power plant. As for any other power plant, it is necessary to compute its production plan. Firstly, we propose in this PhD thesis an architecture and management method for an aggregation power plant composed of any type of energies systems. Then, we propose algorithms to compute the production plan of any types of energy systems satisfying all theirs constraints. Finally, we propose an approach to compute the production plan of the aggregation power plant in order to maximize its financial profit while complying with all the constraints of the grid.

The modal satisfiability problem has to date been solved using either a specifically designed algorithm, or by translating the modal logic formula into a different class of problem, such as a first-order logic, a propositional satisfiability problem or a constraint satisfaction problem. These approaches and the solvers developed to support them are surveyed and a synthesis thereof is presented. The translation of a modal K formula into a constraint satisfaction problem, as developed by Brand et al. [18], is further enhanced. The modal formula, which must be in conjunctive normal form, is translated into layered propositional formulae. Each of these layers is translated into a constraint satisfaction problem and solved using the constraint solver ECLiPSe. I extend this translation to deal with reflexive and transitive accessibility relations, thereby providing for the modal logics KT and S4. Two of the difficulties that arise when these accessibility relations are added are that the resultant formula increases considerably in complexity, and that it is no longer in conjunctive normal form (CNF). I eliminate the need for the conversion of the formula to CNF and deal instead with formulae that are in negation normal form (NNF). I apply a number of enhancements to the formula at each modal layer before it is translated into a constraint satisfaction problem. These include extensive simplification, the assignment of a single value to propositional variables that occur only positively or only negatively, and caching the status of the formula at each node of the search tree. All of these significantly prune the search space. The final results I achieve compare favorably with those obtained by other solvers. / Computing / M.Sc. (Computer Science)

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology.

In today's competitive environment, the importance of continuous production, quality improvement, and fast delivery has forced production and delivery processes to become highly reliable. Keeping equipment in good condition through maintenance activities can ensure a more reliable system. However, maintenance leads to temporary reduction in capacity that could otherwise be utilized for production. Therefore, the coordination of maintenance and production is important to guarantee good system performance. The central thesis of this dissertation is that integrating maintenance and production decisions increases efficiency by ensuring high quality production, effective resource utilization, and on-time deliveries. Firstly, we study the problem of integrated maintenance and production planning where machines are preventively maintained in the context of a periodic review production system with uncertain yield. Our goal is to provide insight into the optimal maintenance policy, increasing the number of finished products. Specifically, we prove the conditions that guarantee the optimal maintenance policy has a threshold type. Secondly, we address the problem of integrated maintenance planning and production scheduling where machines are correctively maintained in the context of a dynamic aircraft repair shop. To solve the problem, we view the dynamic repair shop as successive static repair scheduling sub-problems over shorter periods. Our results show that the approach that uses logic-based Benders decomposition to solve the static sub-problems, schedules over longer horizon, and quickly adjusts the schedule increases the utilization of aircraft in the long term. Finally, we tackle the problem of integrated maintenance planning and production scheduling where machines are preventively maintained in the context of a multi-machine production system. Depending on the deterioration process of machines, we design decomposed techniques that deal with the stochastic and combinatorial challenges in different, coupled stages. Our results demonstrate that the integrated approaches decrease the total maintenance and lost production cost, maximizing the on-time deliveries. We also prove sufficient conditions that guarantee the monotonicity of the optimal maintenance policy in both machine state and the number of customer orders. Within these three contexts, this dissertation demonstrates that the integrated maintenance and production decision-making increases the process efficiency to produce high quality products in a timely manner.