The operation of a human organization requires dozens of everyday tasks to ensure coherence in organizational activities, monitor the status of such activities, gather information relevant to the organization, keep everyone in the organization informed, and so on. Teams of software agents can aid humans in accomplishing these tasks, facilitating the organization's coherent functioning and rapid response to crises and reducing the burden on humans. These activities are often well suited for software agents, which can devote significant resources to perform these tasks, thus reducing the burden on humans. Indeed, teams of such software agents, including proxy agents that act on behalf of humans, would enable organizations to act coherently, attain their mission goals robustly, react to crises swiftly, and adapt to events dynamically. Such agent teams could assist all organizations, including the military, civilian disaster response, corporations, and universities and research institutions.

'm delighted to bring our readers the news of an exciting resource for AAAI members. AAAI has now completed a major initiative, begun five years ago, to develop a digital library of AAAI publications. The collection now comprises approximately 13,000 papers, including the full set of papers from the AAAI proceedings, papers from other major conferences, AAAI workshop and symposium technical reports, selected AAAI Press books, and the full contents of AI Magazine. This already-extensive collection is a growing resource, with new publications and access methods to be added over time. I encourage readers to visit it at the members' library section of the AAAI web site, www.aaai.org.

A complementary challenge is to understand how to design "rules of encounter" (Rosenschein and Zlotkin 1994) by which to promote simple, robust and beneficial interactions between multiple intelligent agents. This is a natural development, as AI is increasingly used for automated decision making in real-world settings. As we extend the ideas of mechanism design from economic theory, the mechanisms (or rules) become algorithmic and many new challenges surface. Starting with a short background on mechanism design theory, the aim of this paper is to provide a nontechnical exposition of recent results on dynamic incentive mechanisms, which provide rules for the coordination of agents in sequential decision problems. The framework of dynamic mechanism design embraces coordinated decision making both in the context of uncertainty about the world external to an agent and also in regard to the dynamics of agent preferences.

In this article, we illustrate the motivations for distributed problem solving and provide an overview of two distributed problem-solving models, namely distributed constraint-satisfaction problems (DCSPs) and distributed constraint-optimization problems (DCOPs), and some of their algorithms. These agents are often assumed to be cooperative, that is, they are part of a team or they are self-interested but incentives or disincentives have been applied such that the individual agent rewards are aligned with the team reward. We illustrate the motivations for distributed problem solving with an example. Imagine a decentralized channel-allocation problem in a wireless local area network (WLAN), where each access point (agent) in the WLAN needs to allocate itself a channel to broadcast such that no two access points with overlapping broadcast regions (neighboring agents) are allocated the same channel to avoid interference. Figure 1 shows example mobile WLAN access points, where each access point is a Create robot fitted with a wireless CenGen radio card. Figure 2a shows an illustration of such a problem with three access points in a WLAN, where each oval ring represents the broadcast region of an access point. This problem can, in principle, be solved with a centralized approach by having each and every agent transmit all the relevant information, that is, the set of possible channels that the agent can allocate itself and its set of neighboring agents, to a centralized server.

Some application domains highlight the importance of distributed continual planning concepts; coordinating teams of unmanned ground vehicles in dynamic environments is an example of such a domain. In this article, I illustrate the ideas in, and promises of, distributed continual planning by showing how acquiring and distributing operator intent among multiple semiautonomous vehicles supports ongoing, cooperative mission elaboration and revision. It is this longer-term view that motivates the use of planning such that an agent should decide between alternative anticipated sequences of activities; otherwise, the application might be better served with simpler reactive agents that only decide on their very next actions. Second, what the agent knows about the application domain, or what the agent's objectives are, or both, can change over time. Information about the domain could be revealed incrementally or could dynamically change in ways outside the agent's control, and thus, the agent should continually reevaluate its ongoing plans and revise or elaborate them to accommodate the changes.

Ten years ago, at the first AAAI conference, Alan Newell (1982), in his presidential address, focused on understanding the then dominant paradigm for artificial intelligence: the writing of symbolic reasoning programs for an agent that would act rationally to achieve a goal. He distinguished the "knowledge level" from the "symbol level" description of a system. The former encompasses the knowledge and conditions necessary for an agent to solve a problem. It is only when the knowledge level is reduced to a symbol level description that issues of implementation get considered. Newell's analysis postulated a single agent, with fixed knowledge and a specified goal.

The term intelligence in the phrase artificial intelligence suggests that intelligence is the key characteristic to be analyzed and synthesized by the research discipline. However, for many researchers the objective of this discipline is the scientific understanding of all aspects of complex behavior. For some, this objective might be limited to the traditional goals of scientific psychology: understanding humans. For others, it might include other species and artificial systems. In either case, the enterprise is driven by psychological questions because humans are the extreme of the known range of possibility that drives our curiosity.

We pay particular attention to the design process, highlighting the objectives and properties that are important in the design of good prediction mechanisms. Whereas game theorists ask what outcome results from a game, mechanism designers ask what game produces a desired outcome. In this sense, game theorists act like scientists and mechanism designers like engineers. In this article, we survey a number of mechanisms created to elicit predictions, many newly proposed within the last decade. We focus on the engineering questions: How do they work and why?

Interacting with a computer requires adopting some metaphor to guide our actions and expectations. Most human-computer interfaces can be classified according to two dominant metaphors: (1) agent and (2) environment. Interactions based on an agent metaphor treat the computer as an intermediary that responds to user requests. In the environment metaphor, a model of the task domain is presented for the user to interact with directly. The term agent has come to refer to the automation of aspects of human-computer interaction (HCI), such as anticipating commands or autonomously performing actions.

As distributed systems of computers play an increasingly important role in society, it will be necessary to consider ways in which these machines can be made to interact effectively. We are concerned with heterogeneous, distributed systems made up of machines that have been programmed by different entities to pursue different goals. Adjusting the rules of public behavior (the rules of the game) by which the programs must interact can influence the private strategies that designers set up in their machines. These rules can shape the design choices of the machines' programmers and, thus, the run-time behavior of their creations. Certain kinds of desirable social behavior can thus be caused to emerge through the careful design of interaction rules.