We present a novel second-order trajectory optimization algorithm based on Stein Variational Newton's Method and Maximum Entropy Differential Dynamic Programming. The proposed algorithm, called Stein Variational Differential Dynamic Programming, is a kernel-based extension of Maximum Entropy Differential Dynamic Programming that combines the best of the two worlds of sampling-based and gradient-based optimization. The resulting algorithm avoids known drawbacks of gradient-based dynamic optimization in terms of getting stuck at local minima, while it overcomes limitations of sampling-based stochastic optimization in terms of introducing undesirable stochasticity when applied in online fashion. To test the efficacy of the proposed algorithm, experiments are conducted in Model Predictive Control mode. The experiments include comparisons with unimodal and multimodal Maximum Entropy Differential Dynamic Programming as well as Model Predictive Path Integral Control and its multimodal and Stein Variational extensions. The results demonstrate the superior performance of the proposed algorithms and confirm the hypothesis that there is a middle ground between sampling-and gradient-based optimization that is indeed beneficial for dynamic optimization.

This study presents an efficient sparse learning-based pattern recognition framework to recognize the discrete states of three emotions—happy, angry, and neutral emotion—using electroencephalogram (EEG) signals. In affective computing with massive spatiotemporal brainwave signals, a large number of features can be extracted to capture various information from multivariate brain data. However, it is often a challenge to model high-dimensional features efficiently in consideration of the intrinsic structure, such as channel location, feature group, time epoch, etc. In this study, features were extensively extracted from EEG signals and were applied on a structured sparse learning model to perform feature selection and classification simultaneously. An efficient ADMM-based algorithm with a closed-form solution was developed to solve the sparse group model. Experimental results show that the proposed method is capable of selecting a small number of important neural features to discriminate the three emotion states with high classification accuracy. With greatly enhanced interpretability and efficiency to learn neural signatures of brain activity from high-dimensional-feature, low-sample-size brain imaging data, the presented computational framework is promising for handling emotion recognition problems with high-dimensional brain imaging data.

This position paper will give an overview of the Business Intelligence (BI) learning framework which includes: (1) BI game; (2) data warehouse system; (3) self-service BI tools, and (4) learning assessment. The BI game is used as an educational platform to simulate business scenarios and business processes. The data warehouse system integrates all of the business transactions and results from the BI game and provides a single point of truth for analytical information. During the business processes, self-service BI tools are used to access data marts for business analytics by both students and instructors. The learning assessment component is used to evaluate students’ knowledge and skills in BI and 21st Century skills.

This position paper presents, firstly, the evolution of decision support systems (DSS) and the challenges in teaching in the field of DSS. Secondly, the concepts of management process, decision support technology, self-service business intelligence (SSBI), business simulation games and literature search results on business games associated with DSS are presented. Lastly, we suggest a conceptual framework of using DSS/SSBI on top of business simulation games to support better decision making.