February 10: IJCAI-03 Electronic After the conference, an expense report Mark your calendars now for IJCAI-poster submission deadline will be required to account for the 03! The Eighteenth International February 12: IJCAI-03 Hard-copy funds awarded.

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.

The infrastructure of modern society is controlled by software systems. These systems are vulnerable to attacks; several such attacks, launched by "recreation hackers," have already led to severe disruption. However, a concerted and planned attack whose goal is to reap harm could lead to catastrophic results (for example, by disabling the computers that control the electrical power grid for a sustained period of time). The survivability of such information systems in the face of attacks is therefore an area of extreme importance to society. This article is set in the context of self-adaptive survivable systems: software that judges the trustworthiness of the computational resources in its environment and that chooses how to achieve its goals in light of this trust model. Each self-adaptive survivable system detects and diagnoses compromises of its resources, taking whatever actions are necessary to recover from attack. In addition, a long-term monitoring system collects evidence from intrusion detectors, firewalls, and all the selfadaptive components, building a composite trust model used by each component. Self-adaptive survivable systems contain models of their intended behavior; models of the required computational resources; models of the ways in which these resources can be compromised; and finally, models of the ways in which a system can be attacked and how such attacks can lead to compromises of the computational resources. In this article, I focus on computational vulnerability analysis: a system that, given a description of a computational environment, deduces all the attacks that are possible. In particular, its goal is to develop multistage attack models in which the compromise of one resource is used to facilitate the compromise of other, more valuable resources. Although the ultimate aim is to use these models online as part of a self-adaptive system, there are other offline uses as well that we are deploying first to help system administrators assess the vulnerabilities of their computing environment.

The Fifteenth Annual International Conference of the Florida Artificial Intelligence Research Society (FLAIRS) was held in Pensacola Beach, Florida, 14 to 16 May 2002. Spanning a broad spectrum of AI research, the conference was composed of a general track and 14 themed special tracks. Conference highlights included invited talks by James Allen, Randall Beer, Jeff Bradshaw, Bill Clancey, Clark Glymour, and Pat Hayes. Two parallel workshops on causality and categorization and studies of expert knowledge and skill followed the conference.

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.

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.

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 Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2002 Spring Symposium Series, held Monday through Wednesday, 25 to 27 March 2002, at Stanford University. The nine symposia were entitled (1) Acquiring (and Using) Linguistic (and World) Knowledge for Information Access; (2) Artificial Intelligence and Interactive Entertainment; (3) Collaborative Learning Agents; (4) Information Refinement and Revision for Decision Making: Modeling for Diagnostics, Prognostics, and Prediction; (5) Intelligent Distributed and Embedded Systems; (6) Logic-Based Program Synthesis: State of the Art and Future Trends; (7) Mining Answers from Texts and Knowledge Bases; (8) Safe Learning Agents; and (9) Sketch Understanding.

We develop, analyze, and evaluate a novel, supervised, specific-to-general learner for a simple temporal logic and use the resulting algorithm to learn visual event definitions from video sequences. First, we introduce a simple, propositional, temporal, event-description language called AMA that is sufficiently expressive to represent many events yet sufficiently restrictive to support learning. We then give algorithms, along with lower and upper complexity bounds, for the subsumption and generalization problems for AMA formulas. We present a positive-examples--only specific-to-general learning method based on these algorithms. We also present a polynomial-time--computable ``syntactic'' subsumption test that implies semantic subsumption without being equivalent to it. A generalization algorithm based on syntactic subsumption can be used in place of semantic generalization to improve the asymptotic complexity of the resulting learning algorithm. Finally, we apply this algorithm to the task of learning relational event definitions from video and show that it yields definitions that are competitive with hand-coded ones.

Much work in AI deals with the selection of proper actions in a given (known or unknown) environment. However, the way to select a proper action when facing other agents is quite unclear. Most work in AI adopts classical game-theoretic equilibrium analysis to predict agent behavior in such settings. This approach however does not provide us with any guarantee for the agent. In this paper we introduce competitive safety analysis. This approach bridges the gap between the desired normative AI approach, where a strategy should be selected in order to guarantee a desired payoff, and equilibrium analysis. We show that a safety level strategy is able to guarantee the value obtained in a Nash equilibrium, in several classical computer science settings. Then, we discuss the concept of competitive safety strategies, and illustrate its use in a decentralized load balancing setting, typical to network problems. In particular, we show that when we have many agents, it is possible to guarantee an expected payoff which is a factor of 8/9 of the payoff obtained in a Nash equilibrium. Our discussion of competitive safety analysis for decentralized load balancing is further developed to deal with many communication links and arbitrary speeds. Finally, we discuss the extension of the above concepts to Bayesian games, and illustrate their use in a basic auctions setup.