This paper focuses on "tracing player knowledge" in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the paper is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called "Parallel" to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error.

Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. However, developing strong QC algorithms has been hindered by the limited size and complexity of annotated data available. To address this, we present the largest challenge dataset for QC, containing 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains. We then show that a BERT-based model trained on this dataset achieves a large (+0.12 MAP) gain compared with previous methods, while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. Finally, we show that using this model's predictions of question topic significantly improves the accuracy of a question answering system by +1.7% P@1, with substantial future gains possible as QC performance improves.

Alexa can read you the latest headlines, give you the weather forecast, and help you find a recipe to make for dinner, but did you know that your Echo device can also assist your student with homework? If you have an Amazon Echo smart speaker at home, like our favorite Echo device, the Echo (2nd Generation), your student has access to a wealth of brain-sharpening, knowledge-enhancing activities to help tackle homework assignments. While kids probably don't need more time interacting with electronics, Alexa offers a variety of skills that can help students with their homework when parents aren't available. To enable a skill on your Echo device say, "Alexa, enable [exact name of skill]." Or, open the Alexa app, tap the menu button, and select "Skills."

The architecture behind Michelangelo uses a modern but complex stack based on technologies such as HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

Artificial Intelligence is spreading its wings almost everywhere. Starting from the businesses to even the agricultural fields, AI is powering the world in many ways than one. There have been various discussions surrounding the fact that AI has the potential to impact the education sector as well. There seems to be various possibilities that Artificial Intelligence is expected to spur innovation in the field of education. We use education as a way to create minds equipped with utilizing and expanding the knowledge pool, whereas Artificial Intelligence provides tools for building up a progressively exact and comprehensive picture of how the human brain performs.

Ever fantasied about having your own personal assistant to answer any questions you can ask, or have conversations with? Well, thanks to Machine Learning and Deep Neural Networks, this is not so far from happening. Think of the amazing capabilities exhibited by Apple's Siri or Amazon's Alexa. Don't get too excited, in this next series of posts we are not going to create an omnipotent Artificial Intelligence, rather we will create a simple chatbot that given some input information and a question about such information, responds to yes/no questions regarding what it has been told. It is nowhere near to Siri's or Alexa's capabilities, but it illustrates very well how even using very simple deep neural network structures, amazing results can be obtained.

To cooperate with humans effectively, virtual agents need to be able to understand and execute language instructions. A typical setup to achieve this is with a scripted teacher which guides a virtual agent using language instructions. However, such setup has clear limitations in scalability and, more importantly, it is not interactive. Here, we introduce an autonomous agent that uses discrete communication to interactively guide other agents to navigate and act on a simulated environment. The developed communication protocol is trainable, emergent and requires no additional supervision. The emergent language speeds up learning of new agents, it generalizes across incrementally more difficult tasks and, contrary to most other emergent languages, it is highly interpretable. We demonstrate how the emitted messages correlate with particular actions and observations, and how new agents become less dependent on this guidance as training progresses. By exploiting the correlations identified in our analysis, we manage to successfully address the agents in their own language.

Deep Reinforcement Learning (DRL) has become a powerful methodology to solve complex decision-making problems. However, DRL has several limitations when used in real-world problems (e.g., robotics applications). For instance, long training times are required and cannot be accelerated in contrast to simulated environments, and reward functions may be hard to specify/model and/or to compute. Moreover, the transfer of policies learned in a simulator to the real-world has limitations (reality gap). On the other hand, machine learning methods that rely on the transfer of human knowledge to an agent have shown to be time efficient for obtaining well performing policies and do not require a reward function. In this context, we analyze the use of human corrective feedback during task execution to learn policies with high-dimensional state spaces, by using the D-COACH framework, and we propose new variants of this framework. D-COACH is a Deep Learning based extension of COACH (COrrective Advice Communicated by Humans), where humans are able to shape policies through corrective advice. The enhanced version of D-COACH, which is proposed in this paper, largely reduces the time and effort of a human for training a policy. Experimental results validate the efficiency of the D-COACH framework in three different problems (simulated and with real robots), and show that its enhanced version reduces the human training effort considerably, and makes it feasible to learn policies within periods of time in which a DRL agent do not reach any improvement.

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: few introductory materials are available, and furthermore the field has struggled with defining itself and building a shared terminology. In this tutorial, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords.

Thanks to new technology that enables them to create simple tools, robots may be on the verge of their own version of the Stone Age. Using a novel capability to reason about shape, function, and attachment of unrelated parts, researchers have for the first time successfully trained an intelligent agent to create basic tools by combining objects. The concept may sound familiar. It's called "MacGyvering," based off the name of a 1980s--and recently rebooted--television series. In the series, the title character is known for his unconventional problem-solving ability using differing resources available to him.