The difficulty of finding information on the World Wide Web by browsing hypertext documents has led to the development and deployment of various search engines and indexing techniques. However, many information-gathering tasks are better handled by finding a referral to a human expert rather than by simply interacting with online information sources. A personal referral allows a user to judge the quality of the information he or she is receiving as well as to potentially obtain information that is deliberately not made public. The process of finding an expert who is both reliable and likely to respond to the user can be viewed as a search through the net-work of social relationships between individuals as opposed to a search through the network of hypertext documents. The goal of the REFERRAL WEB Project is to create models of social networks by data mining the web and develop tools that use the models to assist in locating experts and related information search and evaluation tasks.

I view the World Wide Web as an information food chain. The maze of pages and hyperlinks that comprise the Web are at the very bottom of the chain. The WEBCRAWLERs and ALTAVISTAs of the world are information herbivores; they graze on Web pages and regurgitate them as searchable indices. Today, most Web users feed near the bottom of the information food chain, but the time is ripe to move up. Since 1991, we have been building information carnivores, which intelligently hunt and feast on herbivores in UNIX, on the Internet, and on the Web. Information carnivores will become increasingly critical as the Web continues to grow and as more naive users are exposed to its chaotic jumble.

Robotics Team 1 from Kansas State University was the team that perfectly completed the Office Navigation event in the shortest time at the fifth Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence. The team, consisting of Michael Novak and Darrel Fossett, developed its code in an undergraduate software-engineering course. Its C code used multiple threads to provide separate autonomous agents to solve the meeting scheduling task, control the sonar sensors, and control the actual robot motion. The team's robot software was nicknamed SLICK WILLIE for the way it gracefully moved through doorways and around obstacles.

This article describes JEEVES, one of the winning entries in the 1996 Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence. The model, a geometric map constructed from sensory data gathered while the robot performed its task, enabled JEEVES to sweep the arena efficiently. This article argues that JEEVES's success depended crucially on the existence of the model. It also argues that models are generally useful in mobile robotics -- even in tasks as simple as the one faced in this competition.

This article is the content of an invited talk given by the authors at the Thirteenth National Conference on Artificial Intelligence (AAAI-96). The piece begins with a short history of the competition, then discusses the technical challenges and the political and cultural issues associated with bringing it off every year. We also cover the science and engineering involved with the robot tasks and the educational and commercial aspects of the competition. We finish with a discussion of the community formed by the organizers, participants, and the conference attendees.

The National Aeronautics and Space Administration (NASA) is being challenged to perform more frequent and intensive space-exploration missions at greatly reduced cost. Nowhere is this challenge more acute than among robotic planetary exploration missions that the Jet Propulsion Laboratory (JPL) conducts for NASA. This article describes recent and ongoing work on spacecraft autonomy and ground systems that builds on a legacy of existing success at JPL applying AI techniques to challenging computational problems in planning and scheduling, real-time monitoring and control, scientific data analysis, and design automation.

This article discusses the use of fast (60 frames per second) object tracking using the COGNACHROME VISION SYSTEM, produced by Newton Research Labs. The authors embedded the vision system in a small robot base to tie for first place in the Clean Up the Tennis Court event at the 1996 Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence. Of particular interest is that the authors' entry was the only robot capable of using a gripper to capture and pick up the motorized, randomly moving squiggle ball. Other examples of robotic systems using fast vision tracking are also presented, such as a robot arm capable of catching thrown objects and the soccer-playing robot team that won the 1996 Micro Robot World Cup Soccer Tournament in Taejon, Korea.

The Robot World-Cup Soccer (RoboCup) is an attempt to foster AI and intelligent robotics research by providing a standard problem where a wide range of technologies can be integrated and examined. A robot team must actually perform a soccer game, incorporating various technologies, including design principles of autonomous agents, multiagent collaboration, strategy acquisition, real-time reasoning, robotics, and sensor fusion. RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCup's final target is a world cup with real robots, RoboCup offers a software platform for research on the software aspects of RoboCup.

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.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1996 Fall Symposia Series on 9 to 11 November in Cambridge, Massachusetts. This article contains summaries of the seven symposia that were conducted: (1) Configuration; (2) Developing Assistive Technology for People with Disabilities; (3) Embodied Cognition and Action; (4) Flexible Computation: Results, Issues, and Opportunities; (5) Knowledge Representation Systems Based on Natural Language; (6) Learning Complex Behaviors in Adaptive Intelligent Systems; and (7) Plan Execution: Problems and Issues.