Multi-Armed Bandits for Intelligent Tutoring Systems

We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem. The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children

On probabilistic aspects in the dynamic degradation of ductile materials

Dynamic loadings produce high stress waves leading to the spallation of ductile materials such as aluminum, copper, magnesium or tantalum. The main mechanism used herein to explain the change of the number of cavities with the stress rate is nucleation inhibition, as induced by the growth of already nucleated cavities. The dependence of the spall strength and critical time with the loading rate is investigated in the framework of a probabilistic model. The present approach, which explains previous experimental findings on the strain-rate dependence of the spall strength, is applied to analyze experimental data on tantalum.Comment: 28 pages, 13 figures, 3 table

Likelihood Inference for Large Scale Stochastic Blockmodels with Covariates based on a Divide-and-Conquer Parallelizable Algorithm with Communication

We consider a stochastic blockmodel equipped with node covariate information, that is helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-control approximation of the log-likelihood coupled with a subsampling approach. A key feature of the proposed algorithm is its parallelizability, by processing portions of the data on several cores, while leveraging communication of key statistics across the cores during each iteration of the algorithm. The performance of the algorithm is evaluated on synthetic data sets and compared with competing methods for blockmodel parameter estimation. We also illustrate the model on data from a Facebook derived social network enhanced with node covariate information.Comment: 28 pages, 4 figure

Elements of design of an object-oriented framework prototype for wavelet-based image processing using design patterns

Design patterns and frameworks are increasingly popular techniques for addressing key aspects of the design of complex software systems. Patterns support the reuse of design expertise by articulating the aspects of successful solutions to design problems in a particular context. Frameworks are concrete realizations of groups of patterns that enable code reuse and design reuse control of application-specific structures and behaviors to application developers. Application frameworks encapsulate expertise applicable to a wide range of programs and aim to provide a full range of functionality typically needed by an application thus encompassing a horizontal slice of functionality that can be applied across client domains. Domain frameworks encapsulate expertise in a particular domain, thus encompassing a vertical slice of functionality for a specific problem domain reducing the amount of work that needs to be done to implement domain-specific applications. Wavelets and wavelet transform concepts originated from a synthesis of ideas developed during the last thirty years in engineering, physics, and pure mathematics. Wavelets have been very successful in many scientific and engineering fields and they have led to many successful applications in signal analysis and image processing. In this thesis we are presenting design and implementation elements for the development of an object-oriented application and domain framework prototype for wavelet-based image processing applications using design patterns