Karina Valdivia Delgado

Analysis of an advisor-advisee relationship: an exploratory study of the area of exact and Earth sciences in Brazil.

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.

Real-time dynamic programming for Markov decision processes with imprecise probabilities

Markov Decision Processes have become the standard model for probabilistic planning. However, when applied to many practical problems, the estimates of transition probabilities are inaccurate. This may be due to conflicting elicitations from experts or insufficient state transition information. The Markov Decision Process with Imprecise Transition Probabilities (MDP-IPs) was introduced to obtain a robust policy where there is uncertainty in the transition. Although it has been proposed a symbolic dynamic programming algorithm for MDP-IPs (called SPUDD-IP) that can solve problems up to 22 state variables, in practice, solving MDP-IP problems is time-consuming. In this paper we propose efficient algorithms for a more general class of MDP-IPs, called Stochastic Shortest Path MDP-IPs (SSP MDP-IPs) that use initial state information to solve complex problems by focusing on reachable states. The (L)RTDP-IP algorithm, a (Labeled) Real Time Dynamic Programming algorithm for SSP MDP-IPs, is proposed together with three different methods for sampling the next state. It is shown here that the convergence of (L)RTDP-IP can be obtained by using any of these three methods, although the Bellman backups for this class of problems prescribe a minimax optimization. As far as we are aware, this is the first asynchronous algorithm for SSP MDP-IPs given in terms of a general set of probability constraints that requires non-linear optimization over imprecise probabilities in the Bellman backup. Our results show up to three orders of magnitude speedup for (L)RTDP-IP when compared with the SPUDD-IP algorithm.

ProPAT: A Programming ITS Based on Pedagogical Patterns

Research on cognitive theories about programming learning suggests that experienced programmers solve problems by looking for previous solutions that are related to the new problem and that can be adapted to the current situation. Inspired by these ideas, programming teachers have developed a pattern based programming instruction. In this model, learning can be seen as a process of pattern recognition, which compares experiences from the past with the current situation. In this work, we present a new Eclipse programming environment in which a student can program using a set of pedagogical patterns, i.e., elementary programming patterns recommended by a group of teachers.

Learning to program using hierarchical model-based debugging

Model-based Diagnosis is a well known AI technique that has been applied to software debugging for senior programmers, called Model-Based Software Debugging (MBSD). In this paper we describe the basis of MBSD and show how it can be used for educational purposes. By extending the classical diagnosis technique to a hierarchical approach, we built a programming learning system to allow a student to debug his program in different abstraction levels.

Symbolic bounded real-time dynamic programming

Real-time dynamic programming (RTDP) solves Markov decision processes (MDPs) when the initial state is restricted. By visiting (and updating) only a fraction of the state space, this approach can be used to solve problems with intractably large state space. In order to improve the performance of RTDP, a variant based on symbolic representation was proposed, named sRTDP. Traditional RTDP approaches work best on problems with sparse transition matrices where they can often efficiently achieve e-convergence without visiting all states; however, on problems with dense transition matrices where most states are reachable in one step, the sRTDP approach shows an advantage over traditional RTDP by up to three orders of magnitude, as we demonstrate in this paper. We also specify a new variant of sRTDP based on BRTDP, named sBRTDP, which converges quickly when compared to RTDP variants, since it does less updating by making a better choice of the next state to be visited.

Diagnostic of programs for programming learning tools

It is not easy for a student to develop programming skills and learn how to construct their own problem solving algorithms. Well designed materials and tools can guide programming students knowledge and skill construction. Such tools may allow students to acquire better and faster, the necessary programming skills. In this paper we show the results of some experiments realized on a set of faulty student’s programs using ProPAT_deBUG, an automatic program debugger, based on the Model Based Diagnosis technique of Artificial Intelligence. The results show that during the interactive debugging process it is possible for a student to learn by answering the questions posed by the AI diagnosis system to discriminate its fault hypotheses.

Extreme Risk Averse Policy for Goal-Directed Risk-Sensitive Markov Decision Process

The Goal-Directed Risk-Sensitive Markov Decision Process allows arbitrary risk attitudes for the probabilistic planning problem to reach a goal state. In this problem, the risk attitude is modeled by an expected exponential utility and a risk factor λ. However, the problem is not well defined for every λ, posing the problem of defining the maximum (extreme) value for this factor. In this paper, we propose an algorithm to find this e-extreme risk factor and the corresponding optimal policy.

Robust probabilistic planning with ilao

In probabilistic planning problems which are usually modeled as Markov Decision Processes (MDPs), it is often difficult, or impossible, to obtain an accurate estimate of the state transition probabilities. This limitation can be overcome by modeling these problems as Markov Decision Processes with imprecise probabilities (MDP-IPs). Robust LAO* and Robust LRTDP are efficient algorithms for solving a special class of MDP-IPs where the probabilities lie in a given interval, known as Bounded-Parameter Stochastic-Shortest Path MDP (BSSP-MDP). However, they do not make clear what assumptions must be made to find a robust solution (the best policy under the worst model). In this paper, we propose a new efficient algorithm for BSSP-MDPs, called Robust ILAO* which has a better performance than Robust LAO* and Robust LRTDP, considered the-state-of-the art of robust probabilistic planning. We also define the assumptions required to ensure a robust solution and prove that Robust ILAO* algorithm converges to optimal values if the initial value of all states is admissible.

B^2RTDP: An Efficient Solution for Bounded-Parameter Markov Decision Process

Bounded-parameter Markov decision process (BMDP) can be used to model sequential decision problems, where the transitions probabilities are not completely know and are given by intervals. One of the criteria used to solve that kind of problems is the maxim in, i.e., the best action on the worst scenario. The algorithms to solve BMDPs that use this approach include interval value iteration and an extension of real time dynamic programming (Robust-LRTDP). In this paper, we introduce a new algorithm, named B2RTDP, also based on real time dynamic programming that makes a different choice of the next state to be visited using upper and lower bounds of the optimal value function. The empirical evaluation of the algorithm shows that it converges faster than the state-of-the-art algorithms that solve BMDPs.