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).

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.

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.

In (automated) negotiation systems for self-interested agents, contracts have traditionally been binding. The level of commitment is set by decommitting penalties. To be freed from the contract, an agent simply pays its penalty to the other contract party(ies). We show that despite such strategic decommitting, leveled commitment increases the expected payoffs of all contract parties and can enable deals that are impossible under full commitment.

Much of the economic value of electronic commerce comes from the automation of interactions between businesses and individuals. Game theory is a useful set of tools that can be used by designers of electronic-commerce applications in analyzing and engineering of automated agents and communication protocols. The central theoretical concept used in game theory is the Nash equilibrium. In this article, I show how the outcomes supported by a Nash equilibrium can positively be enlarged using automated negotiations.

Not widely known by the AI community, elevator control has become a major field of application for AI technologies. Techniques such as neural networks, genetic algorithms, fuzzy rules and, recently, multiagent systems and AI planning have been adopted by leading elevator companies not only to improve the transportation capacity of conventional elevator systems but also to revolutionize the way in which elevators interact with and serve passengers. In this article, we begin with an overview of AI techniques adopted by this industry and explain the motivations behind the continuous interest in AI. In the second part, we present in more detail a recent development project to apply AI planning and multiagent systems to elevator control problems.

This article is a reflection on agent-based AI. My contention is that AI research should focus on interactive, autonomous systems, that is, agents. We see how recent developments in (multi-) agent-oriented research have taken us closer to the original AI goal, namely, to build intelligent systems of general competence. I point out several areas such as design description, implementation, reusability, and security that must be developed before agents are universally accepted as the AI of the future.

For many individuals and organizations think example, software programs can be used to about and perform their work. Electronic commerce--the obtain cheaper prices for utilities such as basic conduct of business activities electronically telephone services. A simple program can be by digital media--is now part of installed to monitor and direct long distance everyday business. The user dials the country code, and as sharp falls in the share prices of many "dotcoms" he/she continues to dial the telephone number, since early 2000, electronic commerce is the program contacts various long distance still likely to have a major and lasting effect on providers and negotiates the best deal for its most forms of economic activities. The program can be set up to inform the Advances in web-based technologies further user about the price before the call is connected; support the growth of electronic commerce. In for example, the best rate for this call is nine particular, automation and delegation technologies--known cents a minute with no minimum charge.