In this thesis the action learning and generation problem on a humanoid robot is studied. Our aim is to realize action learning, generation and recognition in one system and our inspiration source is the mirror neuron hypothesis which suggests that action learning, generation and recognition share the same neural circuitry. Dynamic Movement Primitives, an efficient action learning and generation approach, are modified in order to fulfill this aim. The system we developed (1) can learn from multiple demonstrations, (2) can generalize to different conditions, (3) generates actions in a closed-loop and online fashion and (4) can be used for online action recognition. These claims are supported by experiments and the applicability of the developed system in real world is demonstrated through implementing it on a humanoid robot.

Imitation is a fundamental way of acquiring knowledge in human development. In their theory of goal-directed imitation (GOADI), Wohlschl&auml / ger et al. (2003) divide the representation of observed movements into hierarchically organized aspects the highest of which is usually the goal. In a face-to-face imitation task young children usually copy the (spatial) goal of the body movement in terms of perceptual mirror symmetry rather than match them conceptually onto their own body, as adults do. We refer to these imitation schemes as &ldquo / mirroring&rdquo / and &ldquo / matching&rdquo / respectively. In the present study, we investigate the effects of age and perspective of the child with respect to the experimenter (0&deg / , 90&deg / , 180&deg / ) in two imitation tasks, a hand-to-ear and a cup-grasping task. Moreover, we evaluate the developmental changes in the imitative behavior of children from a dynamical systems perspective. Children were supposed to imitate the movements of the experimenter. Tasks were conducted on 4.5- to 11-year-old Iranian pre-school and elementary school children (81 female, 84 male). Imitation scores for the spatial goal were analyzed in terms of mirroring or matching. Imitation schemes varied according to age and perspective in both tasks. Overall, older children&rsquo / s imitations of movements were more adult-like as established by an adult Iranian control group than those of the younger ones. They rather matched than mirrored observed movements. In the 180&deg / and 90&deg / conditions the mirroring scheme was predominant, but in 0&deg / matching was predominant. GOADI was confirmed / however it was qualified by the child&#039 / s perspective on the experimenter. Children&rsquo / s imitations showed a non-linear shift from perceptually-based mirroring to conceptually-based matching of observed movements onto their own body. This shift happens between 6 and 8-9 years of age. The amount of matching depends not only on age but also on control parameters such as spatial perspective, task demands, and exposure.

[pt] O processo de combinação de ações de sintonia fina não possui nem uma formulação precisa, nem uma abordagem formal para solucioná-lo. É necessário definir o que combinar dentre as múltiplas ações existentes e, uma vez escolhidas, como compor de maneira que as restrições sejam verificadas. Trata-se de um problema complexo e relevante na área de bancos de dados, tanto para soluções manuais pelo DBA como automáticas, por meio de softwares especializados. Isto ocorre pois os diferentes tipos de ações de sintonia possuem estratégias distintas para alcançar o objetivo em comum. Esta tese propõe um método automático para geração e seleção de soluções combinadas de sintonia fina para bancos de dados relacionais. Discute-se como combinar soluções e respeitar as restrições tecnológicas e recursos computacionais disponíveis. Por fim, apresenta-se uma implementação e avaliação utilizando três SGBDs de mercado relevantes, em que mostramos tanto a eficácia como a eficiência do método proposto. Os resultados mostraram que o método é capaz de produzir soluções combinadas válidas mais eficientes que soluções locais independentes. / [en] The process of combining database tuning actions has neither a precise formulation nor a formal approach to solving it. It is necessary to define what to combine among multiple existing operations and, once chosen, how to compose so that constraints can be verified. It is a complex and relevant problem in the database research area, both for the DBA manual solutions, and automatic ones using specialized software. It is important because the different types of tuning actions have different strategies to achieve a common goal. This thesis proposes an automated method for generating and selecting combined tuning solutions for relational databases. It discusses how to mix solutions and still respect both the technological constraints and available computational resources. Finally, we present an implementation and evaluation using three relevant market DBMSs, where we show both the effectiveness and the efficiency of the proposed method. The results showed that the technique is capable of producing combined solutions that are more efficient than independent local solutions.