Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms.

In order to advance the field of neuroscience, we must continue motivating youth to pursue science education. In this report we tested the idea of combining neurogaming with education. We developed a pair of electroencephalography (EEG)-controlled neurogames using inexpensive and/or free tools to teach students about the fundamentals of neuroscience and brain machine interfaces (BMI) through a fun, interactive activity. We report on the particular concepts they allowed us to introduce, the techniques and methods we used, and the effect of the activities on stimulating students’ interest in neuroscience, and discuss how to optimize the learning experience. We conclude that educational neurogames could be a key tool for furthering and motivating neuroscience education.

Participants Intelligence (AAAI-15) and the Twenty-Seventh Conference in the AAAI-15 Robotics Exhibition and the on Innovative Applications of Artificial Intelligence AAAI-15 Video Competition are encouraged to contribute (IAAI-15) will be held January 25-29 at the to the Demonstration Program with their systems, Hyatt Regency Austin in Austin, Texas, USA. AAAI is working October 8 (Papers Due) closely with the local AI community to create opportunities The Senior Member Track provides an opportunity for attendees to experience AI in Texas! Attendees for established researchers in the AI community to can also enjoy nearly 200 music venues that feature give a broad talk on a well-developed body of everything from rock and blues to country and research, an important new research area, or a promising jazz every night of the week. Austin cuisine has new topic. This year, new "Blue Sky Ideas" track expanded from barbecue and Tex-Mex to award-winning is seeking presentations aimed at presenting ideas and inventive international cuisine, and blossomed and visions that can stimulate the research community beyond brick-and-mortar restaurants to a to pursue new directions, such as new problems, vibrant, citywide food truck movement.

The Educational Advances in Artificial Intelligence column discusses and shares innovative educational approaches that teach or leverage AI and its many subfields at all levels of education (K-12, undergraduate, and graduate levels). I credit these positive changes to the active in-class learning and a new enthusiasm for teaching, as well as the first-rate lectures by Stanford professors Jennifer Wisdom and Andrew Ng. I was showed that students liked this SPOC format, although pleased when students, enrolled in Introduction to there were suggestions for better in-class and Artificial Intelligence Class MOOC CS188x at the MOOC-content coordination. Had I tweaked my University of California, Berkeley, came to my channel course and continued along this path, I might have for remediation, taking word back to the MOOC's achieved phenominal success, but sadly I left the discussion forum. I required students in my graduate SPOC format behind.

Concept map is a graphical tool for representing knowledge. They have been used in many different areas, including education, knowledge management, business and intelligence. Constructing of concept maps manually can be a complex task; an unskilled person may encounter difficulties in determining and positioning concepts relevant to the problem area. An application that recommends concept candidates and their position in a concept map can significantly help the user in that situation. This paper gives an overview of different approaches to automatic and semi-automatic creation of concept maps from textual and non-textual sources. The concept map mining process is defined, and one method suitable for the creation of concept maps from unstructured textual sources in highly inflected languages such as the Croatian language is described in detail. Proposed method uses statistical and data mining techniques enriched with linguistic tools. With minor adjustments, that method can also be used for concept map mining from textual sources in other morphologically rich languages.

Variational inference is a powerful concept that underlies many iterative approximation algorithms; expectation propagation, mean-field methods and belief propagations were all central themes at the school that can be perceived from this unifying framework. The lectures of Manfred Opper introduce the archetypal example of Expectation Propagation, before establishing the connection with the other approximation methods. Corrections by expansion about the expectation propagation are then explained. Finally some advanced inference topics and applications are explored in the final sections.

Many signal processing and machine learning methods share essentially the same linear-in-the-parameter model, with as many parameters as available samples as in kernel-based machines. Sparse approximation is essential in many disciplines, with new challenges emerging in online learning with kernels. To this end, several sparsity measures have been proposed in the literature to quantify sparse dictionaries and constructing relevant ones, the most prolific ones being the distance, the approximation, the coherence and the Babel measures. In this paper, we analyze sparse dictionaries based on these measures. By conducting an eigenvalue analysis, we show that these sparsity measures share many properties, including the linear independence condition and inducing a well-posed optimization problem. Furthermore, we prove that there exists a quasi-isometry between the parameter (i.e., dual) space and the dictionary's induced feature space.

Many machine learning frameworks, such as resource-allocating networks, kernel-based methods, Gaussian processes, and radial-basis-function networks, require a sparsification scheme in order to address the online learning paradigm. For this purpose, several online sparsification criteria have been proposed to restrict the model definition on a subset of samples. The most known criterion is the (linear) approximation criterion, which discards any sample that can be well represented by the already contributing samples, an operation with excessive computational complexity. Several computationally efficient sparsification criteria have been introduced in the literature, such as the distance, the coherence and the Babel criteria. In this paper, we provide a framework that connects these sparsification criteria to the issue of approximating samples, by deriving theoretical bounds on the approximation errors. Moreover, we investigate the error of approximating any feature, by proposing upper-bounds on the approximation error for each of the aforementioned sparsification criteria. Two classes of features are described in detail, the empirical mean and the principal axes in the kernel principal component analysis.

These are notes from the lecture of Devavrat Shah given at the autumn school "Statistical Physics, Optimization, Inference, and Message-Passing Algorithms", that took place in Les Houches, France from Monday September 30th, 2013, till Friday October 11th, 2013. The school was organized by Florent Krzakala from UPMC & ENS Paris, Federico Ricci-Tersenghi from La Sapienza Roma, Lenka Zdeborova from CEA Saclay & CNRS, and Riccardo Zecchina from Politecnico Torino. This lecture of Devavrat Shah (MIT) covers the basics of inference and learning. It explains how inference problems are represented within structures known as graphical models. The theoretical basis of the belief propagation algorithm is then explained and derived. This lecture sets the stage for generalizations and applications of message passing algorithms.

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. When the teacher is restricted to labeling examples selected by the machine, this problem is an instance of active learning. When the teacher can provide additional information to the machine (e.g., suggestions on what examples or predictive features should be used) as the learning task progresses, then the problem becomes one of interactive learning. To accommodate the two-way communication channel needed for efficient interactive learning, the teacher and the machine need an environment that supports an interaction language. The machine can access, process, and summarize more examples than the teacher can see in a lifetime. Based on the machine's output, the teacher can revise the definition of the task or make it more precise. Both the teacher and the machine continuously learn and benefit from the interaction. We have built a platform to (1) produce valuable and deployable models and (2) support research on both the machine learning and user interface challenges of the interactive learning problem. The platform relies on a dedicated, low-latency, distributed, in-memory architecture that allows us to construct web-scale learning machines with quick interaction speed. The purpose of this paper is to describe this architecture and demonstrate how it supports our research efforts. Preliminary results are presented as illustrations of the architecture but are not the primary focus of the paper.