The goal of an interactive narrative system is to immerse users in a virtual world such that they believe that they are an integral part of an unfolding story and that their actions can significantly alter the direction or outcome of the story. In this article we review the ways in which artificial intelligence can be brought to bear on the creation of interactive narrative systems. We lay out the landscape of about 20 years of interactive narrative research and explore the successes as well as open research questions pertaining to the novel use of computational narrative intelligence in the pursuit of entertainment, education, and training. The prevalence of storytelling in human culture may be explained by the use of narrative as a cognitive tool for situated understanding (Gerrig 1993). This narrative intelligence -- the ability to organize experience into narrative form -- is central to the cognitive processes employed across a range of experiences, from entertainment to active learning.

Collaborative query routing is a new paradigm for web search that treats both established search engines and other publicly available indexes as intelligent peer agents in a search network. The approach makes it transparent for anyone to build his or her own (micro) search engine by integrating established web search services, desktop search, and topical crawling techniques. The challenge in this model is that each of these agents must learn about its environment--the existence, knowledge, diversity, reliability, and trustworthiness of other agents--by analyzing the queries received from and results exchanged with these other agents. We present the 6S peer network, which uses machine-learning techniques to learn about the changing query environment. We show that simple reinforcement learning algorithms are sufficient to detect and exploit semantic locality in the network, resulting in efficient routing and highquality search results.

These fields complement each other: Many statistical methods, particularly those for large data sets, rely on computation, but brute computing power is no substitute for statistical knowledge. Thus, we are seeing the development of intelligent systems for data analysis. To provide an international forum for the discussion of these topics, a series of symposia on IDA was started in 1995 (Liu 1996). In 1997, the Second International Symposium on Intelligent Data Analysis (IDA97) was held at Birkbeck College, University of London, on 4 to 6 August. Almost 130 people from 20 countries in 4 continents attended.

Interactive simulation environments constitute one of today's promising emerging technologies, with applications in areas such as education, manufacturing, entertainment, and training. These environments are also rich domains for building and investigating intelligent automated agents, with requirements for the integration of a variety of agent capabilities but without the costs and demands of low-level perceptual processing or robotic control. Our project is aimed at developing humanlike, intelligent agents that can interact with each other, as well as with humans, in such virtual environments. Our current target is intelligent automated pilots for battlefield-simulation environments. These dynamic, interactive, multiagent environments pose interesting challenges for research on specialized agent capabilities as well as on the integration of these capabilities in the development of "complete" pilot agents. We are addressing these challenges through development of a pilot agent, ...

There is a great dispute among researchers about the roles, characteristics, and specifications of what are called agents, intelligent agents, and adaptive agents. Most research in the field focuses on methodologies for solving specific problems (for example, communications, cooperation, architectures), and little work has been accomplished to highlight and distinguish the field of intelligent agents. As a result, more and more research is cataloged as research on intelligent agents. Therefore, it was necessary to bring together researchers working in the field to define initial boundaries, criteria, and acceptable characteristics of the field. The Workshop on Intelligent Adaptive Agents, presented as part of the Thirteenth National Conference on Artificial Intelligence, addressed these issues as well as many others that are presented in this article.

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.