Made available in DSpace on 2015-03-05T18:36:37Z (GMT). No. of bitstreams: 0 Previous issue date: 17 / Nenhuma / Esta Dissertação propõe o desenvolvimento de uma estrutura hierárquica de gestão que possa ser aplicada em organizações que têm como principal atividade a prestação de serviços em projetos complexos de tecnologia da informação. A proposta se baseia na tradução da literatura tradicional de operações com o detalhamento de uma Estrutura Hierárquica de Planejamento e Controle da Produção e com a consolidação de uma Estrutura Hierárquica de Gestão de Serviços.Para compor o modelo de gestão e por serem os projetos o objeto central da atividade das corporações, é compilada uma Estrutura Hierárquica de Gestão Corporativa de Projetos, que aborda Portfólio, Programas e Projetos com base nas publicações tradicionais dos institutos que contribuem com o desenvolvimento da teoria de gerenciamento de projetos.As características do ambiente de tecnologia da informação e a conceituação de projetos complexos são apresentadas para delimitar a abrangência do estudo e apontar peculiaridades ao modelo proposto.O modelo teórico de / This paper proposes the development of an hierarchical structure of management that is able to be applied in organizations which have service as the main activity in complex projects of information technology.This paper proposes the development of an hierarchical structure of management that is able to be applied in organizations which have service as the main activity in complex projects of information technology.The proposal is based in the translation of traditional literature operations with the detailment of an Hierarchical Structure of Planning and Production Control and with a consolidation of an Hierarchical Structure of Service Management.To compose the management model, and projects being the core object of the corporations’ activity, an Hierarchical Structure of Corporative Project Management is compiled, which approaches Portfolio, Programs and Projects based in traditional publications of institutes that contribute to the development of project management theory.The features of information techno

Robotic motion planning aspires to match the ease and efficiency with which humans move through and interact with their environment. Yet state of the art robotic planners fall short of human abilities; they are slower in computation, and the results are often of lower quality. One stumbling block in traditional motion planning is that points and paths are often considered in isolation. Many planners fail to recognize that substantial shared information exists among path alternatives. Exploitation of the geometric and topological relationships among path alternatives can therefore lead to increased efficiency and competency. These benefits include: better-informed path sampling, dramatically faster collision checking, and a deeper understanding of the trade-offs in path selection. In path sampling, the principle of locality is introduced as a basis for constructing an adaptive, probabilistic, geometric model to influence the selection of paths for collision test. Recognizing that collision testing consumes a sizable majority of planning time and that only collision-free paths provide value in selecting a path to execute on the robot, this model provides a significant increase in efficiency by circumventing collision testing paths that can be predicted to collide with obstacles. In the area of collision testing, an equivalence relation termed local path equivalence, is employed to discover when the work of testing a path has been previously performed. The swept volumes of adjoining path alternatives frequently overlap, implying that a continuum of intermediate paths exists as well. By recognizing such neighboring paths with related shapes and outcomes, up to 90% of paths may be tested implicitly in experiments, bypassing the traditional, expensive collision test and delivering a net 300% boost in collision test performance. Local path equivalence may also be applied to the path selection problem in order to recognize higher-level navigation options and make smarter choices. This thesis presents theoretical and experimental results in each of these three areas, as well as inspiration on the connections to how humans reason about moving through spaces.

Esta dissertação tem como objetivo estudar a combinação de duas técnicas de planejamento em inteligência artificial: planejamento hierárquico e planejamento sob incerteza Knightiana. Cada uma delas possui vantagens distintas, mas que podem ser combinadas, permitindo um ganho de eficiência para o planejamento sob incerteza e maior robustez a planos gerados por planejadores hierárquicos. Primeiramente, estudamos um meio de efetuar uma transformação, de modo sistemático, que permite habilitar algoritmos de planejamento determinístico com busca progressiva no espaço de estados a tratar problemas com ações não-determinísticas, sem considerar a distribuição de probabilidades de efeitos das ações (incerteza Knightiana). Em seguida, esta transformação é aplicada a um algoritmo de planejamento hierárquico que efetua decomposição a partir das tarefas sem predecessoras, de modo progressivo. O planejador obtido é competitivo com planejadores que representam o estado-da-arte em planejamento sob incerteza, devido à informação adicional que pode ser fornecida ao planejador, na forma de métodos de decomposição de tarefas. / This dissertation\'s objective is to study the combination of two artificial intelligence planning techniques, namely: hierarchical planning and planning under Knightian uncertainty. Each one of these has distinct advantages, but they can be combined, allowing the planning under uncertainty a performance gain and giving the hierarchical planning the ability to produce more robust plans. First, we study a way of performing a transformation, in a sistematic way, that enables forward-chaining deterministic planning algorithms to deal with non-deterministic actions, that doesn\'t take into account the probability distribution of actions\' effects (Knightian uncertainty). Afterwards, this transformation is applied to a hierarchical planning algorithm that progressively performs decomposition starting from tasks without predecessors. The obtained planner is competitive with state-of-the-art non-deterministic planners, thanks to the additional information that can be given to the planner, in the form of task decomposition methods.

Esta dissertação tem como objetivo estudar a combinação de duas técnicas de planejamento em inteligência artificial: planejamento hierárquico e planejamento sob incerteza Knightiana. Cada uma delas possui vantagens distintas, mas que podem ser combinadas, permitindo um ganho de eficiência para o planejamento sob incerteza e maior robustez a planos gerados por planejadores hierárquicos. Primeiramente, estudamos um meio de efetuar uma transformação, de modo sistemático, que permite habilitar algoritmos de planejamento determinístico com busca progressiva no espaço de estados a tratar problemas com ações não-determinísticas, sem considerar a distribuição de probabilidades de efeitos das ações (incerteza Knightiana). Em seguida, esta transformação é aplicada a um algoritmo de planejamento hierárquico que efetua decomposição a partir das tarefas sem predecessoras, de modo progressivo. O planejador obtido é competitivo com planejadores que representam o estado-da-arte em planejamento sob incerteza, devido à informação adicional que pode ser fornecida ao planejador, na forma de métodos de decomposição de tarefas. / This dissertation\'s objective is to study the combination of two artificial intelligence planning techniques, namely: hierarchical planning and planning under Knightian uncertainty. Each one of these has distinct advantages, but they can be combined, allowing the planning under uncertainty a performance gain and giving the hierarchical planning the ability to produce more robust plans. First, we study a way of performing a transformation, in a sistematic way, that enables forward-chaining deterministic planning algorithms to deal with non-deterministic actions, that doesn\'t take into account the probability distribution of actions\' effects (Knightian uncertainty). Afterwards, this transformation is applied to a hierarchical planning algorithm that progressively performs decomposition starting from tasks without predecessors. The obtained planner is competitive with state-of-the-art non-deterministic planners, thanks to the additional information that can be given to the planner, in the form of task decomposition methods.

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.