The advanced functionality we seek is illustrated by use during an actual crisis. An emergency coordinator might employ a simulation of the crisis to analyze different potential responses and predict their likely outcomes. Intelligent simulation technology can assist people in such stressful, time-pressured situations to look further ahead in determining the consequences of proposed actions. A key challenge in achieving the potential of simulated worlds is constructing realistic humanlike agents. These agents must be able to coordinate perception, planning, and action, learn, understand and interact with their world, deal with other agents, and use natural language. Even generating a believable animation of a humanlike agent is a complex task: real-time generation of realistic facial expressions is difficult both conceptually and computationally, as is producing coordinated movement of the hundreds of joints in a human body used in such simple actions as getting into a car. Providing all or even a significant portion of this functionality is a challenging mission. However, useful agents can be constructed with only some of these capabilities, even in limited form. For example, early results using the semi-automated OPFOR capability in SIMNET demonstrate the practical utility of even very simple simulated agents. The accumulation of information that is available electronically presents a major dilemma. The good news is that all of the world’s electronic libraries are now at your disposal; the bad news is that you’re on your own, and there’s no one at the information desk. For the NII to be useful, people will have to be able to find information relevant to their problems and tasks in a reasonable amount of time and with reasonable effort. An information-resource specialist system (IRSS) could meet a wide range of needs at home, at work, and at school. Such systems would be tailored to individual users rather than a single project and its needs; consequently, an IRSS would be able to assist its user with a broad range of information needs. The clearest uses of IRSSs are as aids to finding information related to a specific problem. For example, a teacher designing a new course might use his or her IRSS to find relevant background materials, slides to use in his or her presentation, or even information about similar courses taught elsewhere. A rural doctor whose patient presents a rare condition might use his or her IRSS to help assess different treatments or identify new ones or to locate and consult with an appropriate specialist. A neurosurgeon might use his or her IRSS to search a national database for cranial tomography images that resemble the image for a gunshot victim to locate techniques that were used in similar situations and the doctors who used them. An IRSS could help track information over long periods of time. For example, a market analyst for a major department store chain might use his or her IRSS to help identify emerging industry trends. The IRSS would automatically monitor national trade databases and detect and alert its user to changing market patterns. To realize their enormous potential, IRSSs must be powerful, flexible, and easy to use. Users must be able to communicate in whatever way is most natural to them: typing or speaking, for example, in their native language rather than some artificially designed language. The IRSS must allow the use of diagrams and gestures, combining media and modalities in whatever mix is best for getting the message across. The commands that users issue will be general and often vague; nevertheless, the IRSS must accurately determine how to perform such commands. The information that a user needs will often not be stored at any one site; thus, the IRSS will need to be able to access multiple sites and recognize common information. To actively and continuously seek out useful information, an IRSS will need to learn which topics are of long- and short-term interest to each user. Finally, several specialist systems may need to coordinate to locate the relevant information. The world is becoming increasingly complex and information-rich. Not long ago, automobile engines had carburetors with manual adjustments; now they have microprocessor-controlled components and local area networks operating inside the engine compartment. The richness and complexity of these systems are both appealing and daunting. Automobiles can be more energy efficient and less polluting if they are carefully designed and controlled, but complex designs are more difficult to create and harder to debug, modify, and operate. The problems of complexity are nowhere more evident than in the problem of design, modification, and maintenance of the hardware and software that make up complex computer systems. Intelligent project coach systems can address such needs. They could function as coworkers, assisting and collaborating with design or operations teams for complex systems. They could also supply institutional memory. The IPC could remember and recall the rationale of previous decisions and, in times of crisis, explain the methods and reasoning previously used to handle that situation. IPCs would typically incorporate intelligent simulation and information resources systems as components. Extensive and ambitious as this research agenda is, like other strategically defined research, it needs the complement of research that is not strategically defined. Both the problems and the payoff of such research are difficult to predict. But a scientific understanding of information processes such as learning, reasoning, and perceiving could change our understanding of ourselves and the world about us, with consequences that are as difficult to foresee as those of other fundamental breakthroughs in science. Each research thrust area contains a substantial base of existing techniques, tools, and well-defined problems from which to draw. In the sections that follow, we briefly characterize the problem, the research base, and the key scientific challenges for the future.