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Adaptive Nearest Neighbor Classification Using Support Vector Machines

Neural Information Processing Systems

The nearest neighbor technique is a simple and appealing method to address classification problems. It relies on the assumption of locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with a finite number of examples dueto the curse of dimensionality. We propose a technique that computes a locally flexible metric by means of Support Vector Machines (SVMs). The maximum margin boundary found by the SVM is used to determine the most discriminant direction over the query's neighborhood. Such direction provides a local weighting scheme for input features.


Information Self-Service with a Knowledge Base That Learns

AI Magazine

Delivering effective customer service over the internet requires attention to many aspects of knowledge management if it is to be both satisfying for customers and economical for the company or other organization. In RightNow ESERVICE CENTER, such management is built into the architecture and supported by automatically gathering metainformation about the documents held in the core knowledge base. A variety of AI techniques are used to facilitate the construction, maintenance, and navigation of the knowledge base. These techniques include collaborative filtering, swarm intelligence, fuzzy logic, natural language processing, text clustering, and classification rule learning. Customers using ESERVICE CENTER report dramatic decreases in support costs and increases in customer satisfaction because of the ease of use provided by the self-learning features of the knowledge base.


Editorial Introduction: The Fifteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2002)

AI Magazine

The Fourteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2002) was held from 28 July to 1 August in Edmonton, Alberta, Canada, in conjunction with the Seventeenth National Conference on Artificial Intelligence (AAAI-2002). As in past years, papers were solicited in two categories: (1) deployed applications and (2) emerging applications and technologies. Deployed application papers describe systems that have been in use for at least several months by individuals or organizations other than their developers, have measurable benefits, and incorporate AI technologies. Emerging applications are technologies and systems that are close to deployment and clearly show an innovative implementation of AI technologies. These papers are of value not only to other application developers looking for guidance in applying various techniques to their own applications but also to researchers who need to understand the unique technical challenges provided by real-world problems.


Calendar of Events

AI Magazine

Send applications and inquiries to May Cheh; National Library of Medicine, 8600 Rockville Pike, Mail Stop 54, Bethesda, MD 20894-6075; Email: cheh@nlm.nih.gov


The AAAI-02 and IAAI-02 Conferences

AI Magazine

The Eighteenth National Conference on Artificial Intelligence (AAAI-02) and the Fourteenth Conference on Innovative Applications of AI (IAAI- 02) were positively received by those who attended. This report provides a few snapshots of the vast and varied content of the 2002 conferences. Proceedings of AAAI-02 and IAAI-02 are available from AAAI Press (www.- aaaipress.org).


FLAIRS 2002 Conference Report

AI Magazine

ITFlorida will promote the common interests of its members by leveraging their collective talent and advocating on their behalf while formulating policy recommendations to state, federal, and local government. The percent of this year's papers had international semantic web were the most extensive talk included demonstrations of authors. Pat Hayes (UWF-IHMC) gave a Beach, Florida. "view from the trenches" of the ongoing from 14 to 16 May, was sponsored by University) drew an interesting analogy a broad spectrum of research areas. The special tracks presentation themes, which ranged "frictionless brains."


Applying Perceptually Driven Cognitive Mapping to Virtual Urban Environments

AI Magazine

This article describes a method for building a cognitive map of a virtual urban environment. Our routines enable virtual humans to map their environment using a realistic model of perception. We based our implementation on a computational framework proposed by Yeap and Jefferies (1999) for representing a local environment as a structure called an absolute space representation (ASR). Their algorithms compute and update ASRs from a 2-1/2-dimensional (2-1/2D) sketch of the local environment and then connect the ASRs together to form a raw cognitive map.1 Our work extends the framework developed by Yeap and Jefferies in three important ways. First, we implemented the framework in a virtual training environment, the mission rehearsal exercise (Swartout et al. 2001). Second, we developed a method for acquiring a 2- 1/2D sketch in a virtual world, a step omitted from their framework but that is essential for computing an ASR. Third, we extended the ASR algorithm to map regions that are partially visible through exits of the local space. Together, the implementation of the ASR algorithm, along with our extensions, will be useful in a wide variety of applications involving virtual humans and agents who need to perceive and reason about spatial concepts in urban environments.


Training and Using Disciple Agents: A Case Study in the Military Center of Gravity Analysis Domain

AI Magazine

This article presents the results of a multifaceted research and development effort that synergistically integrates AI research with military strategy research and practical deployment of agents into education. It describes recent advances in the DISCIPLE approach to agent development by subject-matter experts with limited assistance from knowledge engineers, the innovative application of DISCIPLE to the development of agents for the strategic center of gravity analysis, and the deployment and evaluation of these agents in several courses at the U.S. Army War College.


MiTAP for Biosecurity: A Case Study

AI Magazine

MITAP (MITRE text and audio processing) is a prototype system available for monitoring infectious disease outbreaks and other global events. MITAP focuses on providing timely, multilingual, global information access to medical experts and individuals involved in humanitarian assistance and relief work. Multiple information sources in multiple languages are automatically captured, filtered, translated, summarized, and categorized by disease, region, information source, person, and organization. Critical information is automatically extracted and tagged to facilitate browsing, searching, and sorting. The system supports shared situational awareness through collaboration, allowing users to submit other articles for processing, annotate existing documents, post directly to the system, and flag messages for others to see. MITAP currently stores over 1 million articles and processes an additional 2,000 to 10,000 daily, delivering up-to-date information to dozens of regular users.


AAAI News

AI Magazine

AAAI's World Wide Web Site serves as a Effective for 1996 the Association has central resource for individuals involved in