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This thesis is a report of a research and design process for creating a layer for a certain Digital Library; a layer that allows users to communicate and socialize with each other within the environment of the Digital Library. Also, the effects that this layer could have on the users’ behaviors, social lives, and private lives, were evaluated.In the process, the most-visited Digital Libraries have been introduced and examined. Furthermore, they have been compared with each other using a united framework. In addition, the user group has been chosen, analyzed and categorized. According to that, International Children’s Digital Library has been chosen as the most suitable Digital Library for the target user group.Through series of prototypes and workshops done with a selection of user group representatives, design decisions were made and tried out. The final outcome of these workshops is a prototype layer for International Children’s Digital Library that allows users to communicate and socialize with one another. As a further matter, the potential effects it could have were explained.

A inteligência artificial não busca somente entender mas construir entidades inteligentes. A inteligência pode ser dividida em vários fatores e um deles é conhecido como aprendizado. A área de aprendizado de máquina visa o desenvolvimento de técnicas para aprendizado automático de máquinas, que incluem computadores, robôs ou qualquer outro dispositivo. Entre essas técnicas encontra-se o Aprendizado por Reforço, foco principal deste trabalho. Mais especificamente, o aprendizado por reforço relacional (ARR) foi investigado, que representa na forma relacional o aprendizado obtido através da interação direta com o ambiente. O ARR é bem interessante no campo de robótica, pois, em geral, não se dispôe do modelo do ambiente e se requer econômia de recursos utilizados. A técnica ARR foi investigada dentro do contexto de aprendizado de uma cabeça robótica. Uma modificação no algoritmo ARR foi proposta, denominada por ETG, e incorporada em uma arquitetura de controle de uma cabeça robótica. A arquitetura foi avaliada no contexto de um problema real não trivial: o aprendizado da atenção compartilhada. Os resultados obtidos mostram que a arquitetura é capaz de exibir comportamentos apropriados durante uma interação social controlada, através da utilização do ETG. Uma análise comparativa com outros métodos foi realizada que mostram que o algoritmo proposto conseguiu obter um desempenho superior na maioria dos experimentos realizados / The artificial Intelligence search not only understand but to build intelligent entities. The intelligence can be divided into several factors and one of them is known as learning. The area of machine learning aimed at the development techniques for automatic learning of machinery, including computers, robots or any other device. Reinforcement Learning is one of those techniques, main focus of this work. Specifically, the relational reinforcement learning was investigated, which is use relational representation for learning obtained through direct interaction with the environment. The relational reinforcement learning is quite interesting in the field of robotics, because, in general, it does not have the model of environment and economy of resources used are required. The relational reinforcement learning technique was investigated within the context of learning a robotic head. A change in the relational reinforcement learning algorithm was proposed, called TGE, and incorporated into an architecture of control of a robotic head. The architecture was evaluated in the context of a real problem not trivial: the learning of shared attention. The results show that the architecture is capable of displaying appropriate behavior during a social interaction controlled through the use of TGE. A comparative analysis was performed with other methods show that the proposed algorithm has achieved a superior performance in most experiments

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A inteligência artificial não busca somente entender mas construir entidades inteligentes. A inteligência pode ser dividida em vários fatores e um deles é conhecido como aprendizado. A área de aprendizado de máquina visa o desenvolvimento de técnicas para aprendizado automático de máquinas, que incluem computadores, robôs ou qualquer outro dispositivo. Entre essas técnicas encontra-se o Aprendizado por Reforço, foco principal deste trabalho. Mais especificamente, o aprendizado por reforço relacional (ARR) foi investigado, que representa na forma relacional o aprendizado obtido através da interação direta com o ambiente. O ARR é bem interessante no campo de robótica, pois, em geral, não se dispôe do modelo do ambiente e se requer econômia de recursos utilizados. A técnica ARR foi investigada dentro do contexto de aprendizado de uma cabeça robótica. Uma modificação no algoritmo ARR foi proposta, denominada por ETG, e incorporada em uma arquitetura de controle de uma cabeça robótica. A arquitetura foi avaliada no contexto de um problema real não trivial: o aprendizado da atenção compartilhada. Os resultados obtidos mostram que a arquitetura é capaz de exibir comportamentos apropriados durante uma interação social controlada, através da utilização do ETG. Uma análise comparativa com outros métodos foi realizada que mostram que o algoritmo proposto conseguiu obter um desempenho superior na maioria dos experimentos realizados / The artificial Intelligence search not only understand but to build intelligent entities. The intelligence can be divided into several factors and one of them is known as learning. The area of machine learning aimed at the development techniques for automatic learning of machinery, including computers, robots or any other device. Reinforcement Learning is one of those techniques, main focus of this work. Specifically, the relational reinforcement learning was investigated, which is use relational representation for learning obtained through direct interaction with the environment. The relational reinforcement learning is quite interesting in the field of robotics, because, in general, it does not have the model of environment and economy of resources used are required. The relational reinforcement learning technique was investigated within the context of learning a robotic head. A change in the relational reinforcement learning algorithm was proposed, called TGE, and incorporated into an architecture of control of a robotic head. The architecture was evaluated in the context of a real problem not trivial: the learning of shared attention. The results show that the architecture is capable of displaying appropriate behavior during a social interaction controlled through the use of TGE. A comparative analysis was performed with other methods show that the proposed algorithm has achieved a superior performance in most experiments

The purpose of this study was to determine the influence of IQ on subtypes of sociable and withdrawn behaviors in children with language impairment (LI). Research has suggested that children with LI are more likely to experience difficulty with social interaction than their typically developing peers (Brinton & Fujiki, 1999; Rice, 1991). The Teacher Behavior Rating Scale (Hart & Robinson, 1996) was used to compare sociable and withdrawn behaviors in 19 children with LI and 19 children with typically developing language. IQ scores for each participant were obtained by administering the Universal Nonverbal Intelligence Test (Bracken & McCallum, 2003). These scores were used as a covariate in group comparisons of sociable and withdrawn behaviors. Comparisons indicated that classroom teachers rated children with LI as displaying more withdrawal and less sociable behaviors than typically developing children even when IQ was controlled.

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