Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This paper seeks to identify the meanings of hand crafting among the kaingang of ëmã (fixed village) Por Fi Ga, located in the municipality of São Leopoldo - RS. The research starts with an ethnographic work that gathered participant observations, interviews, photographs, legal documents and an array of field notes. Accordingly, the research intends to interpret the meaning of hand crafting used by the kaingang, craft that, according to the Amerindians, demands qualities other than economic revenue, which are unfolded in living, education and work. Thus, the research shows the Amerindian perspective about the production of objects and the importance of its learning through the description and analysis of this process, which involves the gathering of materials, division of labors, etc. Among the Amerindian occupation in the city, this paper seeks to contextualize the Kaingang presence during the year 1994, the year of the fixed wãre (temporary camp) consolidation, until the fruition of the ëmã Por Fi Ga, in 2007. It's concluded, from the Amerindian point of view, that the hand crafting is a knowledge that goes throughout the kaingang life and, currently in the city, they revive, through the production of objects, the ways of the ancients and trigger, different from the fóg (non-indians) society, native perspectives about education and work. / Este trabalho busca identificar os significados do fazer artesanato entre os kaingang da ëmã (aldeia fixa) Por Fi Ga, situada no município de São Leopoldo RS. A investigação parte de trabalho etnográfico que reuniu observações participantes, entrevistas, fotografias, documentos jurídicos e um conjunto de anotações de campo. Nesse sentido, a pesquisa propõe interpretar o sentido empregado pelos kaingang no fazer artesanato, fazer que, do ponto de vista dos ameríndios, empenha atributos que extrapolam o ganho econômico e se desdobram no viver, educar e trabalhar. Assim, a pesquisa apresenta a perspectiva ameríndia sobre a produção de objetos e a importância de seu aprendizado por meio da descrição e análise desse processo, que envolve a coleta de material, divisão de tarefas, etc. Acerca da ocupação ameríndia na cidade, o trabalho visa contextualizar a presença Kaingang no período de 1994, ano de consolidação do wãre (acampamento provisório) fixo, até a materialização da ëmã Por Fi Ga, em 2007. Conclui-se, a partir do entendimento ameríndio, que o fazer artesanato é um saber que percorre toda a vida kaingang e, atualmente na cidade, revivem, por meio da produção dos objetos, a maneira dos antigos e acionam, no contraste com a sociedade fóg (não índios), perspectivas nativas sobre educação e trabalho.

The Bayesian network (BN) formalism is a powerful representation for encoding domains characterised by uncertainty. However, before it can be used it must first be constructed, which is a major challenge for any real-life problem. There are two broad approaches, namely the hand-crafted approach, which relies on a human expert, and the data-driven approach, which relies on data. The former approach is useful, however issues such as human bias can introduce errors into the model. We have conducted a literature review of the expert-driven approach, and we have cherry-picked a number of common methods, and engineered a framework to assist non-BN experts with expert-driven construction of BNs. The latter construction approach uses algorithms to construct the model from a data set. However, construction from data is provably NP-hard. To solve this problem, approximate, heuristic algorithms have been proposed; in particular, algorithms that assume an order between the nodes, therefore reducing the search space. However, traditionally, this approach relies on an expert providing the order among the variables --- an expert may not always be available, or may be unable to provide the order. Nevertheless, if a good order is available, these order-based algorithms have demonstrated good performance. More recent approaches attempt to ''learn'' a good order then use the order-based algorithm to discover the structure. To eliminate the need for order information during construction, we propose a search in the entire space of Bayesian network structures --- we present a novel approach for carrying out this task, and we demonstrate its performance against existing algorithms that search in the entire space and the space of orders. Finally, we employ the hand-crafting framework to construct models for the task of diagnosis in a ''real-life'' medical domain, dementia diagnosis. We collect real dementia data from clinical practice, and we apply the data-driven algorithms developed to assess the concordance between the reference models developed by hand and the models derived from real clinical data.