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.

AAAI-14 website or write to us at year.

ICEIS 2014 will be held April 27-30, AAAI Fall Symposium Series. The Twenty-Seventh International Florida 2014 in Lisbon, Portugal AAAI Fall Symposium Series will be AI Research Society Conference. held November 15-17 at the Westin FLAIRS-14 will be held May 21-23, IEA/AIE-14 will be held June 3-6, 2014 Scheduling. ICAPS-14 will be held Computation and Crowdsourcing. KR AAAI Spring Symposium Series will be 2014 will be held July 20-24, 2014 in held March 24-26 at Stanford University Vienna, Austria in Palo Alto, California USA. ICAART 2014 will be held March 6-8, Twenty-Sixth Innovative Applications 2014, in Angers, France of Artificial Intelligence Conference.

We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress in terms of macro and microadaptivity. Macroadaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue and natural language processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student which is needed for maximizing engagement and ultimately learning.

Learner assessment is nontrivial even in its most basic incarnation, namely evaluating a learner's understanding of a set of domain-dependent skills from ad hoc test items (for example, Desmarais [2011]). The assessment challenges increase with the complexity of the learner's traits to be captured, because how a student behaves during an instructional activity generally provides partial and ambiguous information on the student's underlying states, and the gap between what can be observed and what a learner actually thinks and feels increases as these states go from cognitive to metacognitive and affective. In ITSs, the research field concerned with addressing these challenges is known as student modeling, and a student model is the ITS component in charge of assessing student traits and states relevant to tailor the tutorial interaction to specific student needs. Student modeling research has made the problem solution from the tutor et al. [2010]), given extensive evidence substantial progress in providing reliable (for instance by repeatedly asking for in education research showing that learner assessment during problem help) without trying to solve the problem affective factors play an important role solving or question-answering on their own (Baker et al. 2008), in learning. Educational technology At the cognitive level, knowledge can foster understanding at different however, continues to produce novel assessment, that is, evaluating the student's stages of the learning process or for environments often consisting of knowledge of relevant concepts learners with different preferences and activities not as structured and well and skills at specific points of the interaction abilities.

As a present to Mizar on its 40th anniversary, we develop an AI/ATP system that in 30 seconds of real time on a 14-CPU machine automatically proves 40% of the theorems in the latest official version of the Mizar Mathematical Library (MML). This is a considerable improvement over previous performance of large- theory AI/ATP methods measured on the whole MML. To achieve that, a large suite of AI/ATP methods is employed and further developed. We implement the most useful methods efficiently, to scale them to the 150000 formulas in MML. This reduces the training times over the corpus to 1-3 seconds, allowing a simple practical deployment of the methods in the online automated reasoning service for the Mizar users (MizAR).

Graphical models have proven to be powerful tools for representing high-dimensional systems of random variables. One example of such a model is the undirected graph, in which lack of an edge represents conditional independence between two random variables given the rest. Another example is the bidirected graph, in which absence of edges encodes pairwise marginal independence. Both of these classes of graphical models have been extensively studied, and while they are considered to be dual to one another, except in a few instances this duality has not been thoroughly investigated. In this paper, we demonstrate how duality between undirected and bidirected models can be used to transport results for one class of graphical models to the dual model in a transparent manner. We proceed to apply this technique to extend previously existing results as well as to prove new ones, in three important domains. First, we discuss the pairwise and global Markov properties for undirected and bidirected models, using the pseudographoid and reverse-pseudographoid rules which are weaker conditions than the typically used intersection and composition rules. Second, we investigate these pseudographoid and reverse pseudographoid rules in the context of probability distributions, using the concept of duality in the process. Duality allows us to quickly relate them to the more familiar intersection and composition properties. Third and finally, we apply the dualization method to understand the implications of faithfulness, which in turn leads to a more general form of an existing result.