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?

This special issue celebrates the anniversary by presenting perspectives on AAAI's history, on the future of AAAI, and on the past and future of artificial intelligence. It highlights the many voices contributing to AAAI by featuring personal remembrances and visions from many people, including founders of AAAI, presidents who guided the society's development, and others spurring on AI research and applications. While a single issue can only scratch the surface, this special issue clearly illustrates the spirit, accomplishment, and optimism that will drive the next 25 years. It is fitting for AI Magazine to present such a commemoration: 2005 is the twenty-fifth anniversary of the magazine as well. From the beginning, AI Magazine has brought the AI community together to share ideas and advances and to introduce newcomers to the challenges and accomplishments of the field.

In AI, psychology, and education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with varying goals process information differently, studies in education show that goals have a strong effect on what students learn, and functional arguments in machine learning support the necessity of goalbased focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goaldriven learning. This article presents the fundamental points illuminated at the symposium, placing them in the context of open questions and current research directions in goal-driven learning. Learning is a central area of study for researchers interested in human cognition as well as those interested in machine intelligence.

As Hofstadter points out, analogies are fluid, meaning that the analogy between two entities can be drawn differently depending on how these entities are represented. The analogy that is drawn, in turn, can change the representation of the entities being compared. Thus, the analogy between Hofstadter and Sagan can be seen as positive: Both have explained important concepts in their fields to a wide audience and transmitted the excitement of these ideas. Both have inspired a number of people within their fields. Unfortunately, a more negative analogy between Sagan and Hofstadter is possible.

Originally founded in 1987 as a conference to promote and advance AI within the state of Florida, over the years, FLAIRS has attracted national and international participation--56 percent of this year's papers had international authors. After a period of eight years, the Fifteenth International Conference of the Florida Artificial Intelligence Research Society (FLAIRS 2002) returned to the emerald coast of Pensacola Beach, Florida. John Kolen (UWF-IHMC) was the conference general chair, and Susan Haller (University of Wisconsin at Parkside) and Gene Simmons (University of South Alabama) were the program cochairs. FLAIRS is a general conference for reporting AI research, and the 104 papers presented at FLAIRS-2002 covered a broad spectrum of research areas. The conference consisted of 3 parallel sessions of 21 tracks, including 14 special tracks highlighting specific themes.

The First International Workshop on User Modeling in Natural Language Dialogue Systems was held 30-31 August 1986 in Maria Laach, West Germany Issues addressed by the participants included the appropriate contents of a user model, techniques for constructing user models, strategies for reasoning on user models in both understanding and generating natural language dialogue, and the development of general user-modeling systems This article includes an overview of the presentations made at the workshop It is a compilation of the author's impressions and observations and is, therefore, undoubtedly incomplete; and at times might fail to accurately represent the views of the researcher presenting the work The workshop was organized by Dr. Wolfgang Wahlster and Dr. Alfred Kobsa, both of the University of Saarbriicken, and was supported by a grant from the German Science Foundation in its Special Collaborative Program on AI and Knowledge-Based Systems. Twenty-four invited researchers from seven countries participated in the workshop. The program included both long and short talks on current research ideas and projects and lively discussion among the participants; oftentimes, the participants became so engrossed in the presentations and ensuing discussions that other aspects of the program, including the banquet, had to be delayed. But all agreed the workshop had been an enjoyable experience and extremely worthwhile. The workshop program included talks on a wide spectrum of topics related to user modeling in natural language dialogue systems.

Editors' Note: In this provocative article Doyle suggests that I thank Jaime Carbonell, John McDermott, Joseph Schatz, and Derek Sleeman for helpful discussions and comments This research was supported by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 3597, monitored by the Air Force Avionics Laboratory under Contract F33615-81-K-1539. The views and conclusions contained in this document are those of the author, and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Government of the United States of America Abstract, Knowledge engineers qualified to build expert systems are currently in short supply The production of useful and trustworthy cxpcrt systems can he significantly increased by pursuing the idea of nrCiculate ayprentzce.ship This revolution is very important. We now actively seek out tasks for automation that would never have been considered previously. It seems clear that the work of our society and industry includes many economically important (if often mundane) tasks whose automation may be possible with the new techniques.

Early research on AI typically involved qualitative demonstrations of intelligent behavior, with novelty being the primary focus. However, as the field has matured, there have been increasing demands for more careful evaluation using quantitative measures of behavior. In some cases, the response has taken the guise of formal analyses, and in others, it has emphasized comparisons between system and human behavior, but the predominant movement has been toward empirical studies of AI methods. As a result, techniques for experimental design, exploratory data analysis, and statistical testing, originally developed in other fields, have become increasingly relevant for AI researchers. Paul Cohen's book Empirical Methods for Artificial Intelligence aims to encourage this trend by providing AI practitioners with the knowledge and tools needed for careful empirical evaluation.

In the last few years, we have witnessed a major growth in the use of empirical methods in AI. In part, this growth has arisen from the availability of fast networked computers that allow certain problems of a practical size to be tackled for the first time. There is also a growing realization that results obtained empirically are no less valuable than theoretical results. Experiments can, for example, offer solutions to problems that have defeated a theoretical attack and provide insights that are not possible from a purely theoretical analysis. I identify some of the emerging trends in this area by describing a recent workshop that brought together researchers using empirical methods as far apart as robotics and knowledge-based systems.