The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. The KBSE field is concerned with applying knowledge-based AI techniques to the problems of creating, understanding, and maintaining very large software systems. The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. This conference was sponsored by Rome Laboratory (previously Rome Air Development Center) and was held in cooperation with the Association for Computing Machinery and the American Association for Artificial Intelligence. The origin of KBSE-91 is as follows: In 1983, Rome Air Development Center published a report calling for the development of a knowledgebased software assistant (KBSA) that would use AI techniques to support all phases of the software development process (Green et al. 1986).

The Fourth International Conference on Autonomous Agents took place in Barcelona (Catalonia, Spain) from 3 to 7 June 2000, the first one held outside the United States. It had a similar attendance to previous years (435 attendees), thus opening the possibility of other conferences outside the United States. The program committee chairs were Maria Gini, the University of Minnesota, and Jeff Rosenschein, the Hebrew University in Israel. There were 199 submissions from 20 countries, from which 48 papers and 65 posters were selected. Gini managed to obtain some key sponsorship from the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation to support the travel expenses of more than 25 U.S. students.

The events of the Ninth AAAI Robot Competition and Exhibition, held 30 July to 3 August 2000, included the popular Hors d'Oeuvres Anyone? and Challenge events as well as a new event, Urban Search and Rescue. Here, I describe these events as well as the exhibition and the concluding workshop. This year's event brought six contest teams and nine exhibition teams from the United States and Canada. The Robot Contest and Exhibition brings together teams from universities and other laboratories to compete and demonstrate state-ofthe-art research in robotics and AI (figure 1). The contest and exhibit have several goals: (1) encourage students to enter robotics and AI fields at both the undergraduate and graduate levels, (2) increase awareness of the field, and (3) foster the sharing of research ideas and technology. The competition and exhibition is actually made up of multiple events: several contests, a challenge event, an exhibit, and a final workshop for all participants. Descriptions of previous years events can be found in Dean and Bonasso (1993); Konolige (1994); Simmons (1995); Hinkle, Kortenkamp and Miller (1996); Kortenkamp, Nourbakhsh, and Hinkle (1997); Arkin (1998); and Meeden et al. (2000). The competition this year consisted of two events: Hors d'Oeuvres, Anyone? and a new event, Urban Search and Rescue. The event stresses human-robot interaction, as well as mobility, and each contestant is required to explicitly and unambiguously demonstrate interaction with the spectators. The fourth year for this popular event, the robots are judged while they serve finger foods to attendees at the AI Festival. Unlike other contests over the years, there were no artificial walls or constraints in this event--the robots had to interact with regular conference participants, and no attempt was made to limit the number of people interacting with each robot. Robots were judged on the quality of their interactions, coverage, and ability to refill their trays (such as detecting when they needed a refill and navigating to a refill station). In January 2000, a suggestion was made to introduce a new contest, Urban Search and Rescue (USAR).

The growth in the amount of available databases far outstrips the growth of corresponding knowledge. This creates both a need and an opportunity for extracting knowledge from databases. Many recent results have been reported on extracting different kinds of knowledge from databases, including diagnostic rules, drug side effects, classes of stars, rules for expert systems, and rules for semantic query optimization. The importance of this topic is now recognized by leading researchers. Michie predicts that "The next area that is going to explode is the use of machine learning tools as a component of large scale data analysis'' (AI Week, March 15, 1990).

My investigations fall on the experimental psychology side of expert system engineering, specifically the problem of generating methods for extracting the knowledge of experts. I do not review the relevant literature on the cognition of experts.l I want to share a few ideas about research methods that I found worthwhile as I worked with expert interpreters of aerial photographs and other remotely sensed data [Hoffman 1984) and on a project involving expert planners of airlift operations (Hoffman 1986). These ideas should be useful to knowledge engineers and others who might be interested in developing an expert system. In generating expert systems, one must begin by characterizing the knowledge of an expert.

The first Artificial Intelligence (AI) and simulation workshop was held during the National Conference on Artificial Intelligence (AAAI-86) on 11 August 1986 at Wharton Hall, the University of Pennsylvania It was attended by over forty participants from academic, government, and industrial institutions. It included paper presentations, informal discussions, and a panel summary of AI and simulation applications in the areas of: (1) State of the art and future directions in AI and simulation (Authur Gerstenfeld, Worcester Polytechnic Institute); (2) AI problem solving using simulation (Y.V. Ramana Reddy, University of West Virginia); (3) Knowledge representation issues related to simulation (Marilyn Stelzner, Intellicorp); (4) Engineering issues related to AI and simulation (Dick Modjeski, US Army Concepts Analysis Agency). Individual presentations given in each of the above areas of the workshop are published in a technical report distributed by the Defense Technical Information Center (DTIC Number AD-Al74 053). A copy of the report can be obtained by calling DTIC at (202) 274-6847/6874. The intersection of AI and simulation may offer a unique application of computer science that may be of use to both fields.

The Third International Conference on Artificial Intelligence Planning Systems (AIPS-96) was held in Edinburgh, Scotland, from 29 to 31 May 1996. The main gathering of researchers in AI and planning and scheduling, the conference promoted the practical applications of planning technologies. Details of the conference papers and sessions are provided as well as information on the Defense Advanced Research Projects Agency-Rome Laboratory Planning Initiative. Previous conferences were held at the University of Maryland in June 1992 (AIPS-92), organized by Jim Hendler and Drew McDermott, and the University of Chicago in June 1994 (AIPS-94), organized by Kristian Hammond. The generation of plans and related fields, such as scheduling, resource allocation, and reasoning about action, have a long research tradition in AI.

The First National Aeronautics and Space Administration (NASA) Workshop on Monitoring and Diagnosis was held in Pasadena, California, from 15 to 17 January 1992. The workshop brought together individuals from NASA centers, academia, and aerospace who have a common interest in AIbased approaches to monitoring and diagnosis technology. The workshop was intended to promote familiarity, discussion, and collaboration among the research, development, and user communities. The First National Aeronautics and Space Administration (NASA) Workshop on Monitoring and Diagnosis was held in Pasadena, California, from 15 to 17 January 1992. The workshop was hosted by the Jet Propulsion Laboratory (JPL) and took place at the Ritz-Carlton Huntington Hotel.

Department of Computer Science Carnegae-Mellon Unaverszty P&burg, PA 15213 Editors' Note: Many expert systems require some means of handling heuristic rules whose conclusions are less than certain Baysian techniques and other numerical scoring methods have been developed to combine and propagate certainty measures as the expert system draws inferences in solving different problems. Doyle's paper argues that it is difficult for a human expert to produce reliable probabilities or numerical scoring factors for an inference rule, and that a radically different approach to the problem should be considered He essentially suggests that the expert be encouraged to think in terms of specific instances which would conflict with the general rule and to encode this knowledge explicitly. Methodologically this seems to be very appealing, and helps to make both explicit and rigorous some of the techniques currently used by knowledge engineers whm they encode and refine the expert's knowledge We would welcome comments and criticisms of this approach from those steeped in the practical issues of constructing large rule-based expert systems. Probabilistic rules and their variants have recently supported several successful applications of expert systems, in spite of the difficulty of committing informants to particular conditional probabilities or "certainty factors," and in spite of the experimentally observed insensitivity of system performance to perturbations of the chosen values Here we survey recent developments concerning reasoned assumptions which offer hope for avoiding the practical elusiveness of probabilistic rules while retaining theoretical power, for basing systems on the information unhesitatingly gained from expert informants, and reconstructing the entailed degrees of belief later @

Jim Saveland Research Forester Associate Editor, AI Application in Natural Resource Management United States Department of Agriculture Forest Service Southern Forest Fire Laboratory Route 1, Box 182A Dry Branch, GA 31020 Editor: Mr. Saveland's letter focuses our attention on the important distinction between accuracy and realism. We believed the Phoenix fire simulator to be accurate (with the provisos noted in our article). Mr. Saveland believes otherwise, and he is certainly better qualified than us to judge! We can allay some doubts (e.g., firefighting objects actually do move at variable rates, depending on ground cover, as Mr. Saveland notes they should), but basically we agree with Mr. Saveland that the Phoenix fire simulator is not accurate. But we do claim it is realistic.