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).

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."

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.

The staff scheduling problem is a critical problem in the call center (or, more generally, customer contact center) industry. This article describes DIRECTOR, a staff scheduling system for contact centers. DIRECTOR is a constraint-based system that uses AI search techniques to generate schedules that satisfy and optimize a wide range of constraints and service-quality metrics. DIRECTOR has successfully been deployed at more than 800 contact centers, with significant measurable benefits, some of which are documented in case studies included in this article.

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 (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.

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.

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.

This paper presents a new classifier combination technique based on the Dempster-Shafer theory of evidence. The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since each of the available methods that estimates the evidence of classifiers has its own limitations, we propose here a new implementation which adapts to training data so that the overall mean square error is minimized. The proposed technique is shown to outperform most available classifier combination methods when tested on three different classification problems.

Deduction, induction, and abduction [Pei55] are the three basic reasoning mechanisms. Deduction allows drawing conclusions from known facts using some piece of knowledge, so that "battery is down" allows concluding "car will notstart"thanks totheknowledge oftherule"if thebatteryisdown, the car will not start". Induction derives rules from the facts: from the fact that the battery is down and that the car is not starting up, we may conclude the rule relating these two facts. Abduction is the inverse of deduction (to some extent [MF96]): from the fact that the car is not starting up, we conclude that the battery is down. Clearly, this is not the only possible explanation 2 of a car not starting up.