(ICKEDS 2004). This book looks at some of the results of the synergy among AI, cognitive science, and education. Examples include virtual students whose misconceptions force students to reflect on their own knowledge, intelligent tutoring systems, and speech-recognition technology that helps students learn to read. Some of the systems described are already used in classrooms and have been evaluated; a few are still laboratory efforts. The book also addresses cultural and political issues involved in the deployment of new educational technologies.

An adequate natural language description of developments in a real-world scene can be taken as proof of "understanding what is going on." An algorithmic system that generates natural language descriptions from video recordings of road traffic scenes can be said to "understand" its input to the extent that algorithmically generated text is acceptable to the humans judging it. A fuzzy metrictemporal Horn logic (FMTHL) provides a formalism for representing both schematic and instantiated conceptual knowledge about the depicted scene and its temporal development. The resulting conceptual representation mediates in a systematic manner between the spatiotemporal geometric descriptions extracted from video input and a module that generates natural language text. This article outlines a 30-year effort to create such cognitive vision system, indicates its current status, summarizes lessons learned along the way, and discusses open problems against this background.

Jose is also home to a myriad of historic attractions. While most museums deal in antiquity, the Tech Museum of Innovation celebrates technology's cutting edge. The dazzling, mango-colored We are delighted to announce the to aaai04@aaai.org. Please note that 132,000-square-foot domed building continuation of the cooperative effort the deadline for early registrations is serves as a dynamic learning resource with AI Journal, giving unlimited May 28, 2004. Through hands-on exploration, members can view and browse tables Marriott Hotel, in San Jose, California.

Later in the school, the focus turned to ontologies, which is where the true power of the semantic web lies. EUROLAN lecturers treated its potential in terms of what the topic of ontology development it might--and might not--bring to us in the future. This year's and how great its impact will really start somewhere, somehow, even if school was organized by the Faculty be. Although it is not yet clear what emerges is a variety of ontological of Computer Science at the A. I. Cuza whether the current vision of the semantic stores from which to choose. University of Iasi, the Research Institute web will indeed reach its expectations, The EUROLAN summer school also for Artificial Intelligence at the there are more and more included a workshop on ontologies Romanian Academy in Bucharest, opinions that it represents a major and information extraction, a student and the Department of Computer technological step that will permanently workshop on applied natural Science at Vassar College.

NASA Ames Research Center Polish Academy of Sciences URL: www.taai.org.tw/announce/ (PRICAI 2004). (ICKEDS 2004). This book looks at some of the results of the synergy among AI, cognitive science, and education. Examples include virtual students whose misconceptions force students to reflect on their own knowledge, intelligent tutoring systems, and speech recognition technology that helps students learn to read.

Within computational biology, algorithms are constructed with the aim of extracting knowledge from biological data, in particular, data generated by the large genome projects, where gene and protein sequences are produced in high volume. In this article, we explore new ways of representing protein-sequence information, using machine learning strategies, where the primary goal is the discovery of novel powerful representations for use in AI techniques. In the case of proteins and the 20 different amino acids they typically contain, it is also a secondary goal to discover how the current selection of amino acids -- which now are common in proteins -- might have emerged from simpler selections, or alphabets, in use earlier during the evolution of living organisms.

Many control problems take place in continuous state-action spaces, e.g., as in manipulator robotics, where the control objective is often defined as finding a desired trajectory that reaches a particular goal state. While reinforcement learning offers a theoretical framework to learn such control policies from scratch, its applicability to higher dimensional continuous state-action spaces remains rather limited to date. Instead of learning from scratch, in this paper we suggest to learn a desired complex control policy by transforming an existing simple canonical control policy. For this purpose, we represent canonical policies in terms of differential equations with well-defined attractor properties. By nonlinearly transforming the canonical attractor dynamics using techniques from nonparametric regression, almost arbitrary new nonlinear policies can be generated without losing the stability properties of the canonical system. We demonstrate our techniques in the context of learning a set of movement skills for a humanoid robot from demonstrations of a human teacher. Policies are acquired rapidly, and, due to the properties of well formulated differential equations, can be reused and modified online under dynamic changes of the environment. The linear parameterization of nonparametric regression moreover lends itself to recognize and classify previously learned movement skills.

This algorithm-the "single-class minimax probability machine (MPM)"- is built on a distribution-free methodology that minimizes the worst-case probability of a data point falling outside of a convex set, given only the mean and covariance matrix of the distribution and making no further distributional assumptions. We present a robust approach to estimating the mean and covariance matrix within the general two-class MPM setting, and show how this approach specializes to the single-class problem. We provide empirical results comparing the single-class MPM to the single-class SVM and a two-class SVM method. 1 Introduction Novelty detection is an important unsupervised learning problem in which test data are to be judged as having been generated from the same or a different process as that which generated the training data.

In this paper, we study a special kind of learning problem in which each training instance is given a set of (or distribution over) candidate class labels and only one of the candidate labels is the correct one. Such a problem can occur, e.g., in an information retrieval setting where a set of words is associated with an image, or if classes labels are organized hierarchically. We propose a novel discriminative approach for handling the ambiguity of class labels in the training examples. The experiments with the proposed approach over five different UCI datasets show that our approach is able to find the correct label among the set of candidate labels and actually achieve performance close to the case when each training instance is given a single correct label. In contrast, naIve methods degrade rapidly as more ambiguity is introduced into the labels. 1 Introduction Supervised and unsupervised learning problems have been extensively studied in the machine learning literature. In supervised classification each training instance is associated with a single class label, while in unsupervised classification (i.e.

We consider the learning problem of finding a dependency between a general class of objects and another, possibly different, general class of objects. The objects can be for example: vectors, images, strings, trees or graphs. Such a task is made possible by employing similarity measures in both input and output spaces using kernel functions, thus embedding the objects into vector spaces. We experimentally validate our approach on several tasks: mapping strings to strings, pattern recognition, and reconstruction from partial images. 1 Introduction In this article we consider the rather general learning problem of finding a dependency between inputs x E X and outputs y E Y given a training set (Xl,yl),...,(xm, Ym) E X x Y This includes conventional pattern recognition and regression estimation. It also encompasses more complex dependency estimation tasks, e.g mapping of a certain class of strings to a certain class of graphs (as in text parsing) or the mapping of text descriptions to images.