The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this article, we review our work over the last 10 years in the area of supervised learning, focusing on three interlinked directions of research -- (1) theory, (2) engineering applications (making intelligent software), and (3) neuroscience (understanding the brain's mechanisms of learnings) -- that contribute to and complement each other.

We describe an application of a dynamic replanning technique in a highly dynamic and complex domain: the military aeromedical evacuation of patients to medical treatment facilities. Doctrinally, patients requiring extended treatment must be evacuated by air to a suitable medical treatment facility. In early 1993, the DoD tasked USTRANSCOM to consolidate the command and control of medical regulation and aeromedical evacuation operations. The ensuing analysis led to TRAC2ES (TRANSCOM regulating and command and control evacuation system), a decision support system for planning and scheduling medical evacuation operations.

TACAIR-SOAR is an intelligent, rule-based system that generates believable humanlike behavior for large-scale, distributed military simulations. The system is capable of executing most of the airborne missions that the U.S. military flies in fixed-wing aircraft. It accomplishes its missions by integrating a wide variety of intelligent capabilities, including real-time hierarchical execution of complex goals and plans, communication and coordination with humans and simulated entities, maintenance of situational awareness, and the ability to accept and respond to new orders while in flight. The system is currentl y deployed at the Oceana Naval Air Station WISSARD (what-if simulation system for advanced research and development) Lab and the Air Force Research Laboratory in Mesa, Arizona.

Turbine engine diagnostics (TED) is a diagnostic expert system to aid the M1 Abrams tank mechanic find-and-fix problems in the AGT-1500 turbine engine. TED was designed to provide the apprentice mechanic with the ability to diagnose and repair the turbine engine like an expert mechanic. Limited fielding began in 1994 to select U.S. Army National Guard units and complete fielding to all M1 Abrams tank maintenance units started in 1997 and will finish by the end of 1998. The development and fielding of the TED program represents the Army's first successful fielded maintenance system in the area of AI.

The National Association of Securities Dealers, Inc., regulation advanced-detection system (ADS) monitors trades and quotations in The Nasdaq Stock Market to identify patterns and practices of behavior of potential regulatory interest. More important, it has greatly expanded surveillance coverage to new areas of the market and to many new types of behavior of regulatory concern. ADS combines detection and discovery components in a single system that supports multiple regulatory domains and shares the same market data. ADS makes use of a variety of AI techniques, including visualization, pattern recognition, and data mining, in support of the activities of regulatory analysis, alert and pattern detection, and knowledge discovery.

Twenty-six people participated, and 16 papers were presented. The first day was devoted to paper presentations and discussions. The second (half) day, a joint session was held with two other workshops: (1) Building, Maintaining, and Using Organizational Memories and (2) Intelligent Information Integration. The reason for the joint session was that in all three workshops, ontologies play a prominent role, and the goal was to bring together researchers working on related issues in different communities.

To coordinate, intelligent agents might need to know something about themselves, about each other, about how others view themselves and others, about how others think others view themselves and others, and so on. Taken to an extreme, the amount of knowledge an agent might possess to coordinate its interactions with others might outstrip the agent's limited reasoning capacity (its available time, memory, and so on). Much of the work in studying and building multiagent systems has thus been devoted to developing practical techniques for achieving coordination, typically by limiting the knowledge available to, or necessary for, agents. This article categorizes techniques for keeping agents suitably ignorant so that they can practically coordinate and gives a selective survey of examples of these techniques for illustration.

We describe an AI system (CTSHIV) that connects the scientific AIDS literature describing specific human immunodeficiency virus (HIV) drug resistances directly to the customized treatment strategy of a specific HIV patient. Rules in the CTSHIV knowledge base encode knowledge about sequence mutations in the HIV genome that have been found to result in drug resistance to the HIV virus. Rules are applied to the actual HIV sequences of the virus strains infecting the specific patient undergoing clinical treatment to infer current drug resistance. A rule-directed search through mutation sequence space identifies nearby drug-resistant mutant strains that might arise.

Now completing its first year, the High-Performance Knowledge Bases Project promotes technology for developing very large, flexible, and reusable knowledge bases. The project is supported by the Defense Advanced Research Projects Agency and includes more than 15 contractors in universities, research laboratories, and companies.