When modeling real-world decision-theoretic planning problems in the Markov decision process (MDP) framework, it is often impossible to obtain a completely accurate estimate of transition probabilities. For example, natural uncertainty arises in the transition specification due to elicitation of MDP transition models from an expert or data, or non-stationary transition distributions arising from insufficient state knowledge. In the interest of obtaining the most robust policy under transition uncertainty, the Markov Decision Process with Imprecise Transition Probabilities (MDP-IPs) has been introduced to model such scenarios. Unfortunately, while solutions to the MDP-IP are well-known, they require nonlinear optimization and are extremely time-consuming in practice. To address this deficiency, we propose efficient dynamic programming methods to exploit the structure of factored MDP-IPs. Noting that the key computational bottleneck in the solution of MDP-IPs is the need to repeatedly solve nonlinear constrained optimization problems, we show how to target approximation techniques to drastically reduce the computational overhead of the nonlinear solver while producing bounded, approximately optimal solutions. Our results show up to two orders of magnitude speedup in comparison to traditional "flat" dynamic programming approaches and up to an order of magnitude speedup over the extension of factored MDP approximate value iteration techniques to MDP-IPs.

Curricula from the Lattes Platform are a vast source of information for the creation and analysis of researchers' social networks. However, due to the large amount of data, the manual filling-in, and the use of semi-structured data, there are several challenges in the use of Lattes as a source of data. This paper presents a database produced from the mining of more than one million Brazilian Lattes curricula. Moreover, it highlights some descriptive characteristics and relationships among these curricula and among the knowledge areas, directions and challenges to the production and analyzes of social networks generated from these data.

Scientific collaboration has been studied by researchers for decades. Several approaches have been adopted to address the question of how collaboration has evolved in terms of publication output, numbers of coauthors, and multidisciplinary trends. One particular type of collaboration that has received very little attention concerns advisor and advisee relationships. In this paper, we examine this relationship for the researchers who are involved in the area of Exact and Earth Sciences in Brazil and its eight subareas. These pairs are registered in the Lattes Platform that manages the individual curricula vitae of Brazilian researchers. The individual features of these academic researchers and their coauthoring relationships were investigated. We have found evidence that there exists positive correlation between time of advisor–advisee relationship with the advisee’s productivity. Additionally, there has been a gradual decline in advisor–advisee coauthoring over a number of years as measured by the Kulczynski index, which could be interpreted as decline of the dependence.

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