We study online learning in Boolean domains using kernels which capture featureexpansions equivalent to using conjunctions over basic features. Wedemonstrate a tradeoff between the computational efficiency with which these kernels can be computed and the generalization ability ofthe resulting classifier. We first describe several kernel functions which capture either limited forms of conjunctions or all conjunctions. We show that these kernels can be used to efficiently run the Perceptron algorithmover an exponential number of conjunctions; however we also prove that using such kernels the Perceptron algorithm can make an exponential number of mistakes even when learning simple functions. Wealso consider an analogous use of kernel functions to run the multiplicative-update Winnow algorithm over an expanded feature space of exponentially many conjunctions. While known upper bounds imply that Winnow can learn DNF formulae with a polynomial mistake bound in this setting, we prove that it is computationally hard to simulate Winnow's behaviorfor learning DNF over such a feature set, and thus that such kernel functions for Winnow are not efficiently computable.

We study online learning in Boolean domains using kernels which capture feature expansions equivalent to using conjunctions over basic features. We demonstrate a tradeoff between the computational efficiency with which these kernels can be computed and the generalization ability of the resulting classifier. We first describe several kernel functions which capture either limited forms of conjunctions or all conjunctions. We show that these kernels can be used to efficiently run the Perceptron algorithm over an exponential number of conjunctions; however we also prove that using such kernels the Perceptron algorithm can make an exponential number of mistakes even when learning simple functions. We also consider an analogous use of kernel functions to run the multiplicative-update Winnow algorithm over an expanded feature space of exponentially many conjunctions. While known upper bounds imply that Winnow can learn DNF formulae with a polynomial mistake bound in this setting, we prove that it is computationally hard to simulate Winnow's behavior for learning DNF over such a feature set, and thus that such kernel functions for Winnow are not efficiently computable.

In this paper we show that online algorithms for classification and regression can be naturally used to obtain hypotheses with good datadependent tail bounds on their risk. Our results are proven without requiring complicated concentration-of-measure arguments and they hold for arbitrary online learning algorithms. Furthermore, when applied to concrete online algorithms, our results yield tail bounds that in many cases are comparable or better than the best known bounds.

In this paper we show that online algorithms for classification and regression canbe naturally used to obtain hypotheses with good datadependent tailbounds on their risk. Our results are proven without requiring complicated concentration-of-measure arguments and they hold for arbitrary online learning algorithms. Furthermore, when applied to concrete online algorithms, our results yield tail bounds that in many cases are comparable or better than the best known bounds.

Henry Lieberman surveyed successful techniques for programming by example, an approach where end users teach procedures to computers by demonstrating a sequence of actions on concrete examples as they how to accomplish it. This new conference series domain-independent inference practical exercises and illustrated promotes multidisciplinary research structures and reusable domain-specific the concepts with applications, including on tools and methodologies for efficiently ontologies. A related workshop of its knowledge content for communities. He received his Ph.D. in 1. portal.acm.org. For any inquiries, please email info@kcap.org.

The Association for the Advancement of Artificial Intelligence (AAAI) presented the AAAI-02 Workshop Program on Sunday and Monday, 28-29 July 2002 at the Shaw Convention Center in Edmonton, Alberta, Canada. The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The workshops were Agent-Based Technologies for B2B Electronic-Commerce; Automation as a Caregiver: The Role of Intelligent Technology in Elder Care; Autonomy, Delegation, and Control: From Interagent to Groups; Coalition Formation in Dynamic Multiagent Environments; Cognitive Robotics; Game-Theoretic and Decision-Theoretic Agents; Intelligent Service Integration; Intelligent Situation-Aware Media and Presentations; Meaning Negotiation; Multiagent Modeling and Simulation of Economic Systems; Ontologies and the Semantic Web; Planning with and for Multiagent Systems; Preferences in AI and CP: Symbolic Approaches; Probabilistic Approaches in Search; Real-Time Decision Support and Diagnosis Systems; Semantic Web Meets Language Resources; and Spatial and Temporal Reasoning.

Originally introduced them together in a synergistic manner has resulted by Clausewitz in his classical work On in faster progress for each of them. War (1976), the center of gravity is now understood Moreover, it offers a new perspective on how to as representing "those characteristics, capabilities, combine research in AI with research in a specialized or localities from which a military domain and with the development force derives its freedom of action, physical and deployment of prototype systems in education strength, or will to fight" (Joint Chiefs of Staff and practice.

SOAR is one of the oldest and largest AI development efforts, starting formally in 1983. It has also been proposed as a unified theory of cognition (Newell 1990). Most of its current development is as an AI programming language, which was evident at the Twenty-Second SOAR Workshop held at Soar Technology near the University of Michigan in Ann Arbor on 1-2 June 2002.

What are the central issues of CAMD by robots and animals? What does neuroscience tell us about mental development? What computational studies for mental development are needed in neuroscience and psychology? How does a robot chine learning have fruitfully been develop its cognitive and behavioral the budding research area that informed by models of human learning. For example, developmental differ fundamentally from human real physical environment.

The RoboCup Rescue Physical Agent League Competition was held in the summer of 2001 in conjunction with the AAAI Mobile Robot Competition Urban Search and Rescue event, eerily preceding the September 11 World Trade Center (WTC) disaster. Four teams responded to the WTC disaster through the auspices of the Center for Robot-Assisted Search and Rescue (CRASAR), directed by John Blitch. The four teams were Foster- Miller and iRobot (both robot manufacturers from the Boston area), the United States Navy's Space Warfare Center (SPAWAR) group from San Diego, and the University of South Florida (USF). Blitch, through his position as program manager for the Defense Advanced Research Projects Agency (DARPA) Tactical Mobile Robots Program, was a supporter of the competition; he also served as a member of the rules committee and a judge. USF participated by chairing the rules committee, judging, assisting with the logistics, providing commentary, and demonstrating tethered and wireless robots whenever entrants had to skip around during the competition. Based on our experiences and history, we were asked to comment on the validity of the competition. The CRASAR collective experience suggests that most of the basic rules of the competition matched reality because the rules accurately reflected deployment scenarios, but the National Institute of Standards and Technology (NIST) Standard Test Course, and hardware or software approaches forwarded by competitors in last summer's event, missed the mark. This article briefly reviews the types of robots and missions used by CRASAR at the WTC site, then discusses the robotassisted search and rescue effort in terms of lessons for the competition.