In his recent papers, entitled "Intelligence without Representation" and "Intelligence without Reason," Brooks argues for mobile robots as the foundation of AI research. This article argues that even if we seek to investigate complete agents in real-world environments, robotics is neither necessary nor sufficient as a basis for AI research. The article proposes real-world software environments, such as operating systems or databases, as a complementary substrate for intelligent-agent research and considers the relative advantages of software environments as test beds for AI. First, the cost, effort, and expertise necessary to develop and systematically experiment with software artifacts are relatively low. Second, software environments circumvent many thorny but peripheral research issues that are inescapable in physical environments.

The goal of the Workshop on Adaptation and Learning in Multiagent Systems was to focus on research that addresses unique requirements for agents learning and adapting to work in the presence of other agents. Recognizing the applicability and limitations of current machine-learning research as applied to multiagent problems and developing new learning and adaptation mechanisms particularly targeted to this class of problems were the primary research issues that we wanted the authors to address. This article outlines the presentations that were made at the workshop and the success of the workshop in meeting the established goals. Issues that need to be better understood are also presented. Whereas previous research efforts looked at offline design of agent organizations, behavioral rules, negotiation protocols, and so on, it was recognized that agents operating in open, dynamic environments must be able to flexibly adapt to changing demands and opportunities (Lesser 1995).

Other difficult tasks, more generally, are how to obtain a robust performance in teamworks (Cohen and Levesque 1990); how to prevent agents from dropping their commitments; or better, how to regulate agents dropping their commitments to a joint action to not disrupt the common activity and preclude the common goal being achieved (Jennings 1995; Singh 1995; Kinny and Georgeff 1991). These tasks have now entered the MAS field's common knowledge. Other problems are perhaps less obvious. The Second International Conference on Multiagent Systems (ICMAS '96) Workshop on Norms, Obligations, and Conventions was held in Kyoto, Japan, from 10 to 13 December 1996. Participants included scientists from deontic logic, database framework, decision theory, agent architecture, cognitive modeling, and legal expert systems.

I Have a Robot, and I'm Not Afraid to Use It! In this article, I submit that the growing success of robotics at AAMAS is due not only to the nurturing efforts of the AAMAS community, but mainly to the increasing recognition of an important, deeper, truth: it is scientifically useful to roboticists and agent researchers to think of robots as agents. Today, there is a resurgent interest and recognition of the importance of robotics research framed within areas of research familiar to autonomous agents and multiagent systems researchers. Robots (and roboticists) increasingly appear at the AAMAS conferences, and for a good reason. The AAMAS community is investing efforts to encourage robotics research within itself.

We came up with a heavyweight agent architecture, using ideas from AI planning and robotics. These sorts of architectures were very much in vogue at the time, and the company wanted its own, proprietary technology. We started thinking about programming languages for the agents and the kinds of knowledge representation and reasoning that would be required. We spent a lot of time and money flying from London to the U.S. West Coast, talking to patent lawyers. It transpired that the architecture, its decision-making and action models, were completely inappropriate for the problem at hand.

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.