The last few years have seen a surge in interest in the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to engineering their knowledge bases. This special issue reviews recent research in Bayesian problem-solving techniques. The articles cover the topics of inference in Bayesian networks, decision-theoretic planning, and qualitative decision theory. Here, I provide a brief introduction to Bayesian networks and then cover applications of Bayesian problem-solving techniques, knowledge-based model construction and structured representations, and the learning of graphic probability models.

AI researchers are interested in building intelligent machines that can interact with them as they interact with each other. Science fiction writers have given us these goals in the form of HAL in 2001: A Space Odyssey and Commander Data in Star Trek: The Next Generation. However, at present, our computers are deaf, dumb, and blind, almost unaware of the environment they are in and of the user who interacts with them. In this article, I present the current state of the art in machines that can see people, recognize them, determine their gaze, understand their facial expressions and hand gestures, and interpret their activities. I believe that by building machines with such abilities for perceiving, people will take us one step closer to building HAL and Commander Data.

The past five years have seen dramatic advances in planning algorithms, with an emphasis on propositional methods such as GRAPHPLAN and compilers that convert planning problems into propositional conjunctive normal form formulas for solution using systematic or stochastic SAT methods. Related work, in the context of spacecraft control, advances our understanding of interleaved planning and execution. In this survey, I explain the latest techniques and suggest areas for future research.

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. ADS has been in operational use at NASD Regulation since the summer of 1997 by several groups of analysts, processing approximately 2 million transactions a day, generating over 10,000 breaks. 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.

The conference will be held July 18-22, 1999, at the Omni Rosen Hotel and the Orange County Convention Center in Orlando, Florida. National Conference on Artificial by two keynote addresses: (1) AAAI is pleased to announce the Intelligence. This award will honor the author(s) of of AI in other organizations (for example, AAAI is happy to announce its sponsorship paper(s) deemed most influential, CRA, ACM, IEEE); or influential of the CHIKids program during chosen from a specific conference service as a government agency contract AAAI-99. The 1999 award will be given to monitor or program director, provides child care for conference the most influential paper(s) from the resulting in positive effects on the attendees' children, first started two First National Conference on Artificial field of AI. Nominees must be current years ago at the SIGCHI-96.

To not only proving new mathematical obtain broader input, especially from countries results by computer but also formally verifying outside North America, a call for commentaries the correctness of (certain properties of) computer was issued to the automated deduction community.

TACAIR-SOAR is an intelligent, rule-based system that generates believable humanlike behavior for large-scale, distributed military simulations. The innovation of the application is primarily a matter of scale and integration. 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. Its most dramatic use was in the Synthetic Theater of War 1997, which was an operational training exercise that ran for 48 continuous hours during which TACAIR-SOAR flew all U.S. fixed-wing aircraft.