Researchers and technology developers from the National Aeronautics and Space Administration (NASA), other government agencies, academia, and industry recently met in Pasadena, California, to take stock of past and current work and future challenges in the application of AI to highly autonomous systems. In our lifetime, through the eyes of simple robots, grand vistas on other worlds have been unveiled for the first time. Enigmatic questions compel us to go further, to touch these distant landscapes and learn the secrets of the solar system. However, in trying, we find our reach wanting, limited by the link to Earth on which our probes depend. We are learning that to explore further, these probes must go alone, and to go alone, they must become much more intelligent.

The notion of software agents has been around for more than a decade. Since its beginning, the definition of agent, like the definition of intelligence, has been quite controversial and often provoked hot discussions. Questions such as the following normally come up in such arguments: What is an agent? Should a piece of software be categorized as an agent by looking at its behavioral characteristics or by the methodology using which it was produced? Is a printer daemon an agent?

The whole platform is 6 inches high; 12 inches wide, including wheels; and 14 inches long. The 68HC11 processor is in the middle, flanked by two side-pointing sonars. The rest of the sonars are in the front of the robot. The radio modem, with its antenna, is also on top. The housing contains a large battery and the motors.

How do we decide how to represent an intelligent system in its interface, and how do we decide how the interface represents information about the world and about its own workings to a user? The rubric representation covers at least three topics in this context: (1) how a computational system is represented in its user interface, (2) how the interface conveys its representations of information and the world to human users, and (3) how the system's internal representation affects the human user's interaction with the system. I argue that each of these kinds of representation (of the system, information and the world, the interaction) is key to how users make the kind of attributions of intelligence that facilitate their interactions with intelligent systems. In this vein, it makes sense to represent a systmem as a human in those cases where social collaborative behavior is key and for the system to represent its knowledge to humans in multiple ways on multiple modalities. I demonstrate these claims by discussing issues of representation and intelligence in an embodied conversational agent--an interface in which the system is represented as a person, information is conveyed to human users by multiple modalities such as voice and hand gestures, and the internal representation is modality independent and both propositional and nonpropositional.

Software personal assistants continue to be a topic of significant research interest. This article outlines some of the important lessons learned from a successfully deployed team of personal assistant agents (Electric Elves) in an office environment. In the Electric Elves project, a team of almost a dozen personal assistant agents were continually active for seven months. Each elf (agent) represented one person and assisted in daily activities in an actual office environment. This project led to several important observations about privacy, adjustable autonomy, and social norms in office environments. In addition to outlining some of the key lessons learned we outline our continued research to address some of the concerns raised. The goal is to provide software agent assistants for individuals in an office as well as software agents that represent shared office resources. The resulting set of agents coordinate as a team to facilitate routine office activities.

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.