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.

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. The possible combination drug-treatment regimens currently approved by the U.S. Food and Drug Administration are considered and ranked by their estimated ability to avoid identified current and nearby drug-resistant mutants. The highest-ranked treatments are recommended to the attending physician. The result is more precise treatment of individual HIV patients and a decreased tendency to select for drug-resistant genes in the global HIV gene pool. Initial results from a small human clinical trial are encouraging, and further clinical trials are planned. From an AI viewpoint, the case study demonstrates the extensibility of knowledge-based systems because it illustrates how existing encoded knowledge can be used to support new knowledge-based applications that were unanticipated when the original knowledge was encoded.

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. The expert system was designed and built by the U.S. Army Research Laboratory and the U.S. Army Ordnance Center and School. This article discusses the relevant background, development issues, reasoning method, system overview, test results, return on investment, and fielding history of the project. 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 Army estimates that TED will save roughly $10 million a year through improved diagnostic accuracy and reduced waste. The development and fielding of the TED program represents the Army's first successful fielded maintenance system in the area of AI. Several reasons can be given for the success of the TED program: an appropriate domain with proper scope, a close relationship with the expert, extensive user involvement, and others that are discussed in this article.

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. U.S. Transportation Command (USTRANSCOM) is the U.S. Department of Defense (DoD) agency responsible for evacuating patients during wartime and peace. Doctrinally, patients requiring extended treatment must be evacuated by air to a suitable medical treatment facility. The Persian Gulf War was the first significant armed conflict in which this concept was put to a serious test. The results were far from satisfactory -- about 60 percent of the patients ended up at the wrong destinations. 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. Probably the most challenging aspect of the problem has to do with the dynamics of a domain in which requirements and constraints continuously change over time. Continuous dynamic replanning is a key capability of TRAC2ES. This article describes the application and the AI approach we took in providing this capability.

A new policy iteration algorithm for partially observable Markov decision processes is presented that is simpler and more efficient than an earlier policy iteration algorithm of Sondik (1971,1978). The key simplification is representation of a policy as a finite-state controller. This representation makes policy evaluation straightforward. The paper's contribution is to show that the dynamic-programming update used in the policy improvement step can be interpreted as the transformation of a finite-state controller into an improved finite-state controller. The new algorithm consistently outperforms value iteration as an approach to solving infinite-horizon problems.

We study several statistically and biologically motivated learning rules using the same visual environment, one made up of natural scenes, and the same single cell neuronal architecture. This allows us to concentrate on the feature extraction and neuronal coding properties of these rules. Included in these rules are kurtosis and skewness maximization, the quadratic form of the BCM learning rule, and single cell ICA. Using a structure removal method, we demonstrate that receptive fields developed using these rules depend on a small portion of the distribution. We find that the quadratic form of the BCM rule behaves in a manner similar to a kurtosis maximization rule when the distribution contains kurtotic directions, although the BCM modification equations are computationally simpler.

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A new policy iteration algorithm for partially observable Markov decision processes is presented that is simpler and more efficient than an earlier policy iteration algorithm of Sondik (1971,1978). The key simplification is representation of a policy as a finite-state controller. This representation makes policy evaluation straightforward. The paper's contributionis to show that the dynamic-programming update used in the policy improvement step can be interpreted as the transformation ofa finite-state controller into an improved finite-state controller. The new algorithm consistently outperforms value iteration as an approach to solving infinite-horizon problems.

The component gj's are usually relatively simple unimodal densities such as Gaussians. Density estimation with mixtures involves finding the locations, shapes, and weights of the component densities from the data (using for example the Expectation-Maximization (EM) procedure). Kernel density estimation canbe viewed as a special case of mixture modeling where a component is centered at each data point, given a weight of 1/N, and a common covariance structure (kernel shape) is estimated from the data. The quality of a particular probabilistic model can be evaluated by an appropriate scoring rule on independent out-of-sample data, such as the test set log-likelihood (also referred to as the log-scoring rule in the Bayesian literature).