We present a method for binary online classification of triggered but temporally blurredevents that are embedded in noisy time series in the context of online discrimination between left and right imaginary hand-movement. In particular the goal of the binary classification problem is to obtain the decision, as fast and as reliably as possible from the recorded EEG single trials. To provide a probabilistic decision at every time-point t the presented methodgathers information from two distinct sequences of features across time. In order to incorporate decisions from prior time-points we suggest an appropriate weighting scheme, that emphasizes time instances, providing a higher discriminatory power between the instantaneous class distributions of each feature, where the discriminatory power is quantified in terms of the Bayes error of misclassification. The effectiveness of this procedure is verified by its successful application in the 3rd BCI competition. Disclosure of the data after the competition revealed this approach to be superior with single trial error rates as low as 10.7, 11.5 and 16.7% for the three different subjects under study.

The learner then updates its parameter vector to minimize a riskfunctional, and the process repeats.

We describe and analyze an algorithmic framework for online classification where each online trial consists of multiple prediction tasks that are tied together. We tackle the problem of updating the online hypothesis by defining a projection problem in which each prediction task corresponds to a single linear constraint. These constraints are tied together through a single slack parameter. We then introduce ageneral method for approximately solving the problem by projecting simultaneously and independently on each constraint which corresponds to a prediction sub-problem,and then averaging the individual solutions. We show that this approach constitutes a feasible, albeit not necessarily optimal, solution for the original projection problem. We derive concrete simultaneous projection schemes and analyze them in the mistake bound model. We demonstrate the power of the proposed algorithm in experiments with online multiclass text categorization. Our experiments indicate that a combination of class-dependent features with the simultaneous projection method outperforms previously studied algorithms.

Robotics technology has recently advanced to the point of being widely accessible for relatively low-budget research, as well as for graduate, undergraduate, and even secondary and primary school education. This lecture provides an example of how to productively use a cutting-edge advanced robotics platform for education and research by providing a detailed case study with the Sony AIBO robot, a vision-based legged robot. The case study used for this lecture is the UT Austin Villa RoboCup Four-Legged Team. This lecture describes both the development process and the technical details of its end result. The main contributions of this lecture are (i) a roadmap for new classes and research groups interested in intelligent autonomous robotics who are starting from scratch with a new robot, and (ii) documentation of the algorithms behind our own approach on the AIBOs with the goal of making them accessible for use on other vision-based and/or legged robot platforms.

Many problems that arise in machine learning domain deal with nonlinearity and quite often demand users to obtain global optimal solutions rather than local optimal ones. Optimization problems are inherent in machine learning algorithms and hence many methods in machine learning were inherited from the optimization literature. Popularly known as the initialization problem, the ideal set of parameters required will significantly depend on the given initialization values. The recently developed TRUST-TECH (TRansformation Under STability-reTaining Equilibria CHaracterization) methodology systematically explores the subspace of the parameters to obtain a complete set of local optimal solutions. In this thesis work, we propose TRUST-TECH based methods for solving several optimization and machine learning problems. Two stages namely, the local stage and the neighborhood-search stage, are repeated alternatively in the solution space to achieve improvements in the quality of the solutions. Our methods were tested on both synthetic and real datasets and the advantages of using this novel framework are clearly manifested. This framework not only reduces the sensitivity to initialization, but also allows the flexibility for the practitioners to use various global and local methods that work well for a particular problem of interest. Other hierarchical stochastic algorithms like evolutionary algorithms and smoothing algorithms are also studied and frameworks for combining these methods with TRUST-TECH have been proposed and evaluated on several test systems.

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.

Multiagent Systems" was delivered by Mark W. Spong (University of Illinois (ICINCO 2007) "Toward Human-Machine Cooperation" was delivered by Patrick Millot (Université de Valenciennes, France), who is a renowned professor in Europe. ICINCO received 435 paper submissions, not including workshops, from more than 50 countries, in all continents. Sciences et Techniques de l'Ingénieur are researchers in one of the ICIN-and Robotics (ICINCO 2007) was The conference was also held in cooperation selected for poster presentation. The volume tracks: "Intelligent Control Systems will be published by Springer-Verlag. Autònoma de Barcelona, as a major international forum The two satellite workshops were Spain) and Janan Zaytoon (Centre de to debate technical and scientific the Third International Workshop on Recherche en Sciences et Technologies advances presented by researchers and Multiagent Robotic Systems (MARS de l'Information et de la Communication, developers both from academe and 2007), chaired by Joaquim Filipe, and Universite de Reims Champagne-industry.

Artificial Intelligence (IAAI-08) will be system.

Marvin Lee Minsky, a founder of the field of artificial intelligence and professor at MIT, celebrated his 80th birthday on August 9, 2007. This article seizes an opportune time to honor Marvin and his contributions and influence in artificial intelligence, science, and beyond. The article provides readers with some personal insights of Minsky from Danny Hillis, John McCarthy, Tom Mitchell, Erik Mueller, Doug Riecken, Aaron Sloman, and Patrick Henry Winston -- all members of the AI community that Minsky helped to found. The article continues with a brief resume of Minsky's research, which spans an enormous range of fields. It concludes with a short biographical account of Minsky's personal history.

John McCarthy's contributions to computer science and artificial intelligence are legendary. He invented Lisp, made substantial contributions to early work in timesharing and the theory of computation, and was one of the founders of artificial intelligence and knowledge representation. This article, written in honor of McCarthy's 80th birthday, presents a brief biography, an overview of the major themes of his research, and a discussion of several of his major papers.