A set of probabilistic predictions is well calibrated if the events that are predicted to occur with probability p do in fact occur about p fraction of the time. Well calibrated predictions are particularly important when machine learning models are used in decision analysis. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.

Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging. The Approximate Bayesian Computation (ABC) framework is the standard statistical tool to handle these likelihood free problems, but they require a very large number of simulations. In this work we develop two new ABC sampling algorithms that significantly reduce the number of simulations necessary for posterior inference. Both algorithms use confidence estimates for the accept probability in the Metropolis Hastings step to adaptively choose the number of necessary simulations. Our GPS-ABC algorithm stores the information obtained from every simulation in a Gaussian process which acts as a surrogate function for the simulated statistics. Experiments on a challenging realistic biological problem illustrate the potential of these algorithms.

Graphical modelling has a long history in statistics as a tool for the analysis of multivariate data, starting from Wright's path analysis and Gibbs' applications to statistical physics at the beginning of the last century. In its modern form, it was pioneered by Lauritzen and Wermuth and Pearl in the 1980s, and has since found applications in fields as diverse as bioinformatics, customer satisfaction surveys and weather forecasts. Genetics and systems biology are unique among these fields in the dimension of the data sets they study, which often contain several hundreds of variables and only a few tens or hundreds of observations. This raises problems in both computational complexity and the statistical significance of the resulting networks, collectively known as the "curse of dimensionality". Furthermore, the data themselves are difficult to model correctly due to the limited understanding of the underlying mechanisms. In the following, we will illustrate how such challenges affect practical graphical modelling and some possible solutions.

Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterated strategy or direct strategy. This study proposes a novel multiple-step-ahead time series prediction approach which employs multiple-output support vector regression (M-SVR) with multiple-input multiple-output (MIMO) prediction strategy. In addition, the rank of three leading prediction strategies with SVR is comparatively examined, providing practical implications on the selection of the prediction strategy for multi-step-ahead forecasting while taking SVR as modeling technique. The proposed approach is validated with the simulated and real datasets. The quantitative and comprehensive assessments are performed on the basis of the prediction accuracy and computational cost. The results indicate that: 1) the M-SVR using MIMO strategy achieves the best accurate forecasts with accredited computational load, 2) the standard SVR using direct strategy achieves the second best accurate forecasts, but with the most expensive computational cost, and 3) the standard SVR using iterated strategy is the worst in terms of prediction accuracy, but with the least computational cost.

This article focuses on contributions that AI can make to address long-term educational goals. It describes five challenges that would support: (1) mentors for every learner; (2) learning twenty-first century skills; (3) interaction data to support learning; (4) universal access to global classrooms; and (5) lifelong and life-wide learning. A vision and brief research agenda are described for each challenge along with goals that lead to access to global educational resources and the reuse and sharing of digital educational resources. Instructional systems with AI technology are described that currently support richer experiences for learners and supply researchers with new opportunities to analyze vast data sets of instructional behavior from big databases, containing elements of learning, affect, motivation, and social interaction.

This spring, AI Magazine launched its digital edition, which brought the magazine new interactivity and color throughout. I'm delighted to announce that this issue inaugurates another major delivery advance, the launch of the AI Magazine app.

Articulating thought in computer-based media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an Intelligent Learning Environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. This article presents an overview of the DynaLearn system.

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14–15, 2013 at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA.

Part Two of the 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.

Intelligent game-based learning environments integrate commercial game technologies with AI methods from intelligent tutoring systems and intelligent narrative technologies. This article introduces the CRYSTAL ISLAND intelligent game-based learning environment, which has been under development in the authors' laboratory for the past seven years. After presenting CRYSTAL ISLAND, the principal technical problems of intelligent game-based learning environments are discussed: narrative-centered tutorial planning, student affect recognition, student knowledge modeling, and student goal recognition. Solutions to these problems are illustrated with research conducted with the CRYSTAL ISLAND learning environment.