Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.

This special issue of AI Magazine presents articles on some of the most interesting projects at the intersection of AI and Education. Included are articles on integrated systems such as virtual humans, an intellgent textbook a game-based learning environment as well as technology focused components such as student models and data mining. The issue concludes with an article summarizing the contemporary and emerging challenges at the intersection of AI and education.

Traditionally focused on good old-fashioned AI and robotics, the Spanish AI community holds a vigorous computational intelligence substrate. Neuromorphic, evolutionary, or fuzzylike systems have been developed by many research groups in the Spanish computer sciences. It is no surprise, then, that these naturegrounded efforts start to emerge, enriching the AI catalogue of research projects and publications and, eventually, leading to new directions of basic or applied research. In this article, we review the contribution of Melomics in computational creativity.

This special issue of AI Magazine presents articles on some of the most interesting projects at the intersection of AI and Education. Included are articles on integrated systems such as virtual humans, an intellgent textbook a game-based learning environment as well as technology focused components such as student models and data mining. The issue concludes with an article summarizing the contemporary and emerging challenges at the intersection of AI and education.

Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.

Classification is an important topic in statistics and machine learning with great potential in many real applications. In this paper, we investigate two popular large margin classification methods, Support Vector Machine (SVM) and Distance Weighted Discrimination (DWD), under two contexts: the high-dimensional, low-sample size data and the imbalanced data. A unified family of classification machines, the FLexible Assortment MachinE (FLAME) is proposed, within which DWD and SVM are special cases. The FLAME family helps to identify the similarities and differences between SVM and DWD. It is well known that many classifiers overfit the data in the high-dimensional setting; and others are sensitive to the imbalanced data, that is, the class with a larger sample size overly influences the classifier and pushes the decision boundary towards the minority class. SVM is resistant to the imbalanced data issue, but it overfits high-dimensional data sets by showing the undesired data-piling phenomena. The DWD method was proposed to improve SVM in the high-dimensional setting, but its decision boundary is sensitive to the imbalanced ratio of sample sizes. Our FLAME family helps to understand an intrinsic connection between SVM and DWD, and improves both methods by providing a better trade-off between sensitivity to the imbalanced data and overfitting the high-dimensional data. Several asymptotic properties of the FLAME classifiers are studied. Simulations and real data applications are investigated to illustrate the usefulness of the FLAME classifiers.

Bros. The competition has four tracks. Almost as important is that good scoring mechanisms are available, that the visual aspects of the games make it easy to compare and characterize the performance of the controllers, and that it is easy to engage both students and the general public in the competition. Several recently introduced competitions are based on games such as Ms. Pac-Man (Lucas 2007), the first-person shooter Unreal Tournament (Hingston 2010), the real-time strategy game Star-Craft, and the car racing game TORCS (Loiacono et al. 2010). In 2009, Julian Togelius and Sergey Karakovskiy set out to create a benchmark for game AI controllers based on Infinite Mario Bros (IMB). IMB is an open source clone (created by Markus Persson, who later went on to create Minecraft) of Nintendo's platform game Super Mario Bros. (SMB), which has been one of the world's most influential games since its release in 1985.

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.

Traditionally focused on good old-fashioned AI and robotics, the Spanish AI community holds a vigorous computational intelligence substrate. Neuromorphic, evolutionary, or fuzzylike systems have been developed by many research groups in the Spanish computer sciences. It is no surprise, then, that these naturegrounded efforts start to emerge, enriching the AI catalogue of research projects and publications and, eventually, leading to new directions of basic or applied research. In this article, we review the contribution of Melomics in computational creativity.

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