### How AI, AR, and Big Data Will Change the Future of Education - DZone AI

Education has always been a hot topic among intellectuals and reformers. It has seen quite a change in the last decade or so, but not significant enough to get noticed. The new era of learning is still focused on keeping students in the classroom in the hopes that they will bring a better future to themselves and to society as a whole. The current education system has always been focused on a batch study where individual growth is never focused on. With the expansion of the internet, things have changed drastically, as now, anyone can do self-study using YouTube, Udacity, or TED.

### The Many Faces of Exponential Weights in Online Learning

A standard introduction to online learning might place Online Gradient Descent at its center and then proceed to develop generalizations and extensions like Online Mirror Descent and second-order methods. Here we explore the alternative approach of putting exponential weights (EW) first. We show that many standard methods and their regret bounds then follow as a special case by plugging in suitable surrogate losses and playing the EW posterior mean. For instance, we easily recover Online Gradient Descent by using EW with a Gaussian prior on linearized losses, and, more generally, all instances of Online Mirror Descent based on regular Bregman divergences also correspond to EW with a prior that depends on the mirror map. Furthermore, appropriate quadratic surrogate losses naturally give rise to Online Gradient Descent for strongly convex losses and to Online Newton Step. We further interpret several recent adaptive methods (iProd, Squint, and a variation of Coin Betting for experts) as a series of closely related reductions to exp-concave surrogate losses that are then handled by Exponential Weights. Finally, a benefit of our EW interpretation is that it opens up the possibility of sampling from the EW posterior distribution instead of playing the mean. As already observed by Bubeck and Eldan, this recovers the best-known rate in Online Bandit Linear Optimization.

### An Online Learning Method for Improving Over-subscription Planning

Despite the recent resurgence of interest in learning methods for planning, most such efforts are still focused exclusively on classical planning problems. In this work, we investigate the effectiveness of learning approaches for improving over-subscription planning, a problem that has received significant recent interest. Viewing over-subscription planning as a domain-independent optimization problem, we adapt the STAGE (Boyan and Moore 2000) approach to learn and improve the plan search. The key challenge in our study is how to automate the feature generation process. In our case, we developed and experimented with a relational feature set, based on Taxonomic syntax as well as a propositional feature set, based on ground-facts. The feature generation process and training data generation process are all automatic, making it a completely domain-independent optimization process that takes advantage of online learning. In empirical studies, our proposed approach improved upon the baseline planner for over-subscription planning on many of the benchmark problems.

### An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles

Energy-efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model energy consumption at road-segments for efficient navigation. In order to learn the model parameters, we develop an online learning framework and investigate several exploration strategies such as Thompson Sampling and Upper Confidence Bound. We then extend our online learning framework to multi-agent setting, where multiple vehicles adaptively navigate and learn the parameters of the energy model. We analyze Thompson Sampling and establish rigorous regret bounds on its performance. Finally, we demonstrate the performance of our methods via several real-world experiments on Luxembourg SUMO Traffic dataset.