Face detection is a canonical example of a rare event detection problem, inwhich target patterns occur with much lower frequency than nontargets. Outof millions of face-sized windows in an input image, for example, only a few will typically contain a face. Viola and Jones recently proposed a cascade architecture for face detection which successfully addresses therare event nature of the task. A central part of their method is a feature selection algorithm based on AdaBoost. We present a novel cascade learning algorithm based on forward feature selection which is two orders of magnitude faster than the Viola-Jones approach and yields classifiers of equivalent quality. This faster method could be used for more demanding classification tasks, such as online learning.

We consider situations where training data is abundant and computing resources are comparatively scarce. We argue that suitably designed online learningalgorithms asymptotically outperform any batch learning algorithm. Both theoretical and experimental evidences are presented.

Many real-world domains are relational in nature, consisting of a set of objects related to each other in complex ways. This paper focuses on predicting the existence and the type of links between entities in such domains. We apply the relational Markov network framework of Taskar et al. to define a joint probabilistic modelover the entire link graph -- entity attributes and links. The application of the RMN algorithm to this task requires the definition of probabilistic patterns over subgraph structures. We apply this method to two new relational datasets, one involving university webpages, and the other a social network. We show that the collective classification approach of RMNs, and the introduction of subgraph patterns over link labels, provide significant improvements in accuracy over flat classification, which attempts to predict each link in isolation.

Online algorithms for classification often require vast amounts of memory andcomputation time when employed in conjunction with kernel functions. In this paper we describe and analyze a simple approach for an on-the-fly reduction of the number of past examples used for prediction. Experiments performed with real datasets show that using the proposed algorithmic approach with a single epoch is competitive with the support vectormachine (SVM) although the latter, being a batch algorithm, accesses each training example multiple times.

We present a unified view for online classification, regression, and uniclass problems.This view leads to a single algorithmic framework for the three problems. We prove worst case loss bounds for various algorithms for both the realizable case and the non-realizable case. A conversion of our main online algorithm to the setting of batch learning is also discussed. Theend result is new algorithms and accompanying loss bounds for the hinge-loss.

AAAI's Nineteenth National Conference on Artificial Intelligence (AAAI-04) filled the top floor of the San Jose Convention Center from July 25-29, 2004. The week's program was full of recent advances in many different AI research areas, as well as emerging applications for AI. Within the various topics discussed at the conference, a number of strategic domains emerged where AI is being harnessed, including counterterrorism, space exploration, robotics, the Web, health care, scientific research, education, and manufacturing.

Cognition: The Mathematics of Mind. (CORES 2005). Stefanie Bruninghaus, University of Pittsburgh The ICCBR'05 Program Committee invites submissions of original theoretical research, Industry Day Chair: applied research and deployed application (MDAI 2005). Must have a Masters Degree in Data Mining.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2004 Spring Symposium Series, Monday through Wednesday, March 22-24, at Stanford University. The titles of the eight symposia were (1) Accessible Hands-on Artificial Intelligence and Robotics Education; (2) Architectures for Modeling Emotion: Cross-Disciplinary Foundations; (3) Bridging the Multiagent and Multirobotic Research Gap; (4) Exploring Attitude and Affect in Text: Theories and Applications; (5) Interaction between Humans and Autonomous Systems over Extended Operation; (6) Knowledge Representation and Ontologies for Autonomous Systems; (7) Language Learning: An Interdisciplinary Perspective; and (8) Semantic Web Services. Each symposium had limited attendance. Most symposia chairs elected to create AAAI technical reports of their symposium, which are available as paperbound reports or (for AAAI members) are downloadable on the AAAI members-only Web site. This report includes summaries of the eight symposia, written by the symposia chairs.

Joseph then to students who are actively February 19: IS Demos proposals E. Beck (joseph.beck@cmu.edu)

Project Halo is a multistaged effort, sponsored by Vulcan Inc, aimed at creating Digital Aristotle, an application that will encompass much of the world's scientific knowledge and be capable of applying sophisticated problem solving to answer novel questions. Vulcan envisions two primary roles for Digital Aristotle: as a tutor to instruct students in the sciences and as an interdisciplinary research assistant to help scientists in their work. As a first step towards this goal, we have just completed a six-month pilot phase designed to assess the state of the art in applied knowledge representation and reasoning (KR&/R). Vulcan selected three teams, each of which was to formally represent 70 pages from the advanced placement (AP) chemistry syllabus and deliver knowledge-based systems capable of answering questions on that syllabus. The evaluation quantified each system's coverage of the syllabus in terms of its ability to answer novel, previously unseen questions and to provide human- readable answer justifications. These justifications will play a critical role in building user trust in the question-answering capabilities of Digital Aristotle. Prior to the final evaluation, a "failure taxonomy' was collaboratively developed in an attempt to standardize failure analysis and to facilitate cross-platform comparisons. Despite differences in approach, all three systems did very well on the challenge, achieving performance comparable to the human median. The analysis also provided key insights into how the approaches might be scaled, while at the same time suggesting how the cost of producing such systems might be reduced. This outcome leaves us highly optimistic that the technical challenges facing this effort in the years to come can be identified and overcome. This article presents the motivation and longterm goals of Project Halo, describes in detail the six-month first phase of the project -- the Halo Pilot -- its KR&R challenge, empirical evaluation, results, and failure analysis. The pilot's outcome is used to define challenges for the next phase of the project and beyond.