However, what are the application areas include autonomous spacecraft key research topics? There are the scientific disciplines support, failure analysis and on-board diagnosis such as physics and chemistry that develop of vehicle systems, automated generation theories, and there are engineering disciplines of control software for photocopiers, and intelligent aids for learning about thermodynamic that develop solutions that change the cycles. Qualitative reasoning is thus relevant physical world. Both use formal mathematical for researchers who are interested in important systems, as well as computer implementations, AI issues as well as for managers, to derive conclusions about natural and artificial developers, and engineers who are looking for pieces of the world. Does this approach potential industrial benefits of AI. not provide a systematic and formal way to A decade has passed since the publication of reason about the physical world? What remains three collections of papers and a book covering to be done for AI research in this area?

Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent's ability to learn useful behaviors by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. We propose and study a formal model of implicit imitation that can accelerate reinforcement learning dramatically in certain cases. Roughly, by observing a mentor, a reinforcement-learning agent can extract information about its own capabilities in, and the relative value of, unvisited parts of the state space. We study two specific instantiations of this model, one in which the learning agent and the mentor have identical abilities, and one designed to deal with agents and mentors with different action sets. We illustrate the benefits of implicit imitation by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability and possible interactions, we briefly comment on extensions of the model that relax these restricitions.

For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the training examples and/or the computational costs associated with learning from them. In such circumstances, one question of practical importance is: if only n training examples can be selected, in what proportion should the classes be represented? In this article we help to answer this question by analyzing, for a fixed training-set size, the relationship between the class distribution of the training data and the performance of classification trees induced from these data. We study twenty-six data sets and, for each, determine the best class distribution for learning. The naturally occurring class distribution is shown to generally perform well when classifier performance is evaluated using undifferentiated error rate (0/1 loss). However, when the area under the ROC curve is used to evaluate classifier performance, a balanced distribution is shown to perform well. Since neither of these choices for class distribution always generates the best-performing classifier, we introduce a "budget-sensitive" progressive sampling algorithm for selecting training examples based on the class associated with each example. An empirical analysis of this algorithm shows that the class distribution of the resulting training set yields classifiers with good (nearly-optimal) classification performance.

However, all eligible students The American Association for Artificial Technical Papers, Workshop Proposals, are encouraged to apply. Intelligence presents the 2004 Tutorial Forum Proposals, Student After the conference, an expense Spring Symposium Series, to be held Programs, Intelligent Systems report will be required to account for Monday through Wednesday, March Demonstrations, and other related the funds awarded.

WEBODE is a scalable workbench for ontological engineering that eases the design, development, and management of ontologies and includes middleware services to aid in the integration of ontologies into real-world applications. WEBODE presents a framework to integrate new ontology-based tools and services, where developers only worry about the new logic they want to provide on top of the knowledge stored in their ontologies.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2003 Spring Symposium Series, Monday through Wednesday, 24-26 March 2003, at Stanford University. The titles of the eight symposia were Agent-Mediated Knowledge Management, Computational Synthesis: From Basic Building Blocks to High- Level Functions, Foundations and Applications of Spatiotemporal Reasoning (FASTR), Human Interaction with Autonomous Systems in Complex Environments, Intelligent Multimedia Knowledge Management, Logical Formalization of Commonsense Reasoning, Natural Language Generation in Spoken and Written Dialogue, and New Directions in Question-Answering Motivation.

Chair: Terry Payne (trp@ecs.soton.ac.uk) nators should contact candidates prior Tentative Organizing AI Alert newsletter, which highlights they be elected. The deadline for Committee: Lloyd Greenwald selected features from the "AI in the nominations is November 1, 2003. Please mark your calendars now for Stanford University. Be sure Symposia/symposia.html) and will be and the Sixteenth Innovative Applications to visit the AI Topics web site at mailed to all AAAI members. Submissions of Artificial Intelligence Conference www.aaai.org/AITopics/aitopics.html will be due to the organizers on (IAAI-04)!

Successes and Challenges" by Alon Halevy, University of Washington; "Constraint Satisfaction, Santa Cruz; "Self-Reconfiguring Robots: Challenges and Successes" by Daniela Rus, Dartmouth University; "Automated Verification Graphs, Automata, and Logic" by Moshe Vardi, "Quantum Information: Fundamentals the world have gathered each summer One hundred the conference is always after conference hours. Also among this year's invited As part of the American Association As In addition to the strong technical to advance the science and practice part of a special track on AI and the track of the conference, the Fifteenth of AI, the organization continues web, Henzinger will discuss the future Innovative Applications of AI conference to play a leading role in organizing of search engines on the internet, (IAAI-03) will be collocated with and sponsoring these annual summer describing work under way to IJCAI. Through the years, we have conferences. Other invited talks include "Deploying in a broad range of computer systems, Information Agents on the machinery, and electronic devices Because AI is an umbrella term for a of Southern California; "Web However, where is the newest, most This book looks at some of the results of this 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.

This article reports on an extensive survey and analysis of research work related to machine learning as it applies to automated planning over the past 30 years. Major research contributions are broadly characterized by learning method and then descriptive subcategories. Survey results reveal learning techniques that have extensively been applied and a number that have received scant attention. We extend the survey analysis to suggest promising avenues for future research in learning based on both previous experience and current needs in the planning community.

The RoboCupJunior division of RoboCup is now entering its third year of international participation and is growing rapidly in size and popularity. This article first outlines the history of the junior league since it was first demonstrated in Paris at RoboCup-1998 and describes how it has evolved into the international sensation it is today. Although the popularity of the event is self-evident, we are working to identify and quantify the educational benefits of the initiative. The remainder of the article focuses on describing our efforts to encapsulate these qualities, highlighting results from a pilot study conducted at RoboCupJunior-2000 and presenting new data from a subsequent study of RoboCupJunior-2001.