Several authors have suggested viewing boosting as a gradient descent search for a good fit in function space. We apply gradient-based boosting methodology to the unsupervised learning problem of density estimation. We show convergence properties of the algorithm and prove that a strength of weak learnability property appliesto this problem as well. We illustrate the potential of this approach through experiments with boosting Bayesian networks to learn density models.

Jason Weston, Olivier Chapelle, Andre Elisseeff, Bernhard Scholkopf and Vladimir Vapnik* Max Planck Institute for Biological Cybernetics, 72076 Tubingen, Germany *NEC Research Institute, Princeton, NJ 08540 USA Abstract 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 betweeninputs x E X and outputs y E Y given a training set (Xl,yl), ...,(xm, Ym) 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.

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?

Traditional approaches to ecological modeling, based on mathematical equations, are hampered by the qualitative nature of ecological knowledge. In this article, we demonstrate that qualitative reasoning provides alternative and productive ways for ecologists to develop, organize, and implement models. We present a qualitative theory of population dynamics and use this theory to capture and simulate commonsense theories about population and community ecology. Advantages of this approach include the possibility of deriving relevant conclusions about ecological systems without numeric data; a compositional approach that enables the reusability of models representing partial behavior; the use of a rich vocabulary describing objects, situations, relations, and mechanisms of change; and the capability to provide causal interpretations of system behavior.

We argue that qualitative modeling provides a valuable way for students to learn. Two modelbuilding environments, VMODEL and HOMER/- VISIGARP, are presented that support learners by constructing conceptual models of systems and their behavior using qualitative formalisms. Both environments use diagrammatic representations to facilitate knowledge articulation. Preliminary evaluations in educational settings provide support for the hypothesis that qualitative modeling tools can be valuable aids for learning.

Program (July 28) is currently accepting nominations accessible to the general public or Tenth AAAI/SIGART Doctoral Consortium for AAAI Fellow. The AAAI Fellows to a broad AI audience (not just a subarea), (July 25-26) program is designed to written within the last two AAAI Intelligent Systems Demonstrations recognize people who have made significant, years.

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.

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.